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K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 51494 edureka!
1. Applications of NLP and Downloading Stanford Core NLP Server in Telugu
 
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In this video I will be explaining few applications of NLP and I will be showing from where to download the stanford core nlp server in Telugu. Links: https://stanfordnlp.github.io/CoreNLP/corenlp-server.html https://nlp.stanford.edu/
Views: 105 For U
Download All Engineering Books For Free
 
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This is a tutorial video for the website www.Hackmykaam.com Watch this video to download all engineering and B.Sc. related books in pdf format for free. • So Please Watch This Full Video to Know more • Hit the like button! • Share this video with your friends. • Feel free to write in comment section if you have any doubt or any suggestions for us.  Subscribe Our Channel: Channel link: https://www.youtube.com/c/hacktechwala3  Like Our Facebook Page : https://www.facebook.com/hacktechwala/  Join Our Google+ Community: https://plus.google.com/communities/104565411307918159287  Follow Us On Instagram : https://www.instagram.com/hacktechwala  Visit Our Blog: https://www.hacktechwala.blogspot.com Please subscribe our channel and press the bell icon to get notified about our latest videos. Don’t forget to watch our previous videos. Thank You!How to download Ebooks for free on Android Watch this video to know more and please drop a like as a token of appreciation. Subscribe HackTech Wala for more such videos. Like our facebook page Follow us on instagram @hacktechwala Checkout our blog: www.hacktechwala.blogspot.com ******************************************************************** Music: http://www.bensound.com -~-~~-~~~-~~-~- Please watch: "Get Huge Discount Online | Flipkart | Amazon | Festive Season Sale | Use Link in description only" https://www.youtube.com/watch?v=9WkOMe1psb0 -~-~~-~~~-~~-~-
Views: 326696 HackTech Wala
YOLO Object Detection (TensorFlow tutorial)
 
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You Only Look Once - this object detection algorithm is currently the state of the art, outperforming R-CNN and it's variants. I'll go into some different object detection algorithm improvements over the years, then dive into YOLO theory and a programmatic implementation using Tensorflow! Code for this video: https://github.com/llSourcell/YOLO_Object_Detection Please Subscribe! And like. And comment. That's what keeps me going. Want more inspiration & education? Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://pjreddie.com/darknet/yolo/ https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/ http://machinethink.net/blog/object-detection-with-yolo/ https://github.com/pjreddie/darknet/wiki/YOLO:-Real-Time-Object-Detection https://github.com/KleinYuan/easy-yolo https://medium.com/@xslittlegrass/almost-real-time-vehicle-detection-using-yolo-da0f016b43de https://medium.com/diaryofawannapreneur/yolo-you-only-look-once-for-object-detection-explained-6f80ea7aaa1e Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 745451 Siraj Raval
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 168673 Siraj Raval
Natural Language Processing in Artificial Intelligence in Hindi | NLP Easy Explanation
 
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Hello Friends Welcome to Well Academy In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Form For Artificial Intelligence Topics Request : https://goo.gl/forms/suL3639o2TG8aKkG3 Artificial Intelligence Full Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fug7z_YlD9M0x8gdZ7ziXen DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 99525 Well Academy
Data Processing: Missing Data (Last Part)
 
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Download dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. You should check this video tutorial to easily download Anaconda Navigator for Python Distribution. https://youtu.be/4v7Uke37QGs First of all, you have to download Anaconda Navigator Distribution for Python. For this go to this link and download for your computer depending on your operating system, Windows, Linux or Mac. https://www.anaconda.com/download/ We have used Python 3.6 Version for our course. So you should download that to cope up with us. The next video: https://www.youtube.com/watch?v=BnmqT8ABvbg&index=5&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe Data Proessing Complete Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe The previous video:https://www.youtube.com/watch?v=gOLgidPEclA&index=3&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DataSet #XY #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #DataProcessing #MissingData #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE #BestBanglaMachineLearningTutorialSeries #ML #MachineLearning
Views: 648 Planeter
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 455298 Thales Sehn Körting
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 224776 Augmented Startups
Learn Python - Full Course for Beginners [Tutorial]
 
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This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗If you liked this video, Mike accepts donations on his website: https://www.mikedane.com/contribute/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 6722271 freeCodeCamp.org
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 135247 nptelhrd
Application of Link Analysis to Transactional Data in SAS Enterprise Miner 12.3
 
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Ye Liu describes the application of link analysis to transactional data in SAS Enterprise Miner 12.3.
Views: 4994 SAS Software
Application Demo (Social Media Mining and Analysis)
 
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Application Demo (Social Media Mining and Analysis) Anggi Perwitasari - Thesis
Views: 48 Anggi Perwitasari
C Programming Tutorial | Learn C programming | C language
 
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C Programming Language is the most popular computer language and most used programming language till now. It is very simple and elegant language. 1) This is by far the most comprehensive C Programming course you'll find here, or anywhere else. 2) This C Programming tutorial Series starts from the very basics and covers advanced concepts as we progress. This course breaks even the most complex applications down into simplistic steps. 3) It is aimed at complete beginners, and assumes that you have no programming experience whatsoever. 4) This C Programming tutorial Series uses Visual training method, offering users increased retention and accelerated learning. Every programmer should and must have learnt C whether it is a Java or C# expert, Because all these languages are derived from C. In this tutorial you will learn all the basic concept of C programming language. Every section in this tutorial is downloadable for offline learning. Topics will be added additional to the tutorial every week or the other which cover more topics and with advanced topics. This is we will Learn Data Types, Arithmetic, If, Switch, Ternary Operator, Arrays, For Loop, While Loop, Do While Loop, User Input, Strings, Functions, Recursion, File I/O, Exceptions, Pointers, Reference Operator , memory management, pre-processors and more. #Ctutorialforbeginners #Ctutorial #Cprogramming #Cprogrammingtutorial #Cbasicsforbeginners c tutorial for beginners. C programming tutorials for beginners. C Programming Language Tutorials Time: 00:12:35 - Lesson 2 - C programming introduction and first ‘hello world’ program Time: 00:25:45 - Lesson 3 - simple input & output ( printf, scanf, placeholder ) Time: 00:41:07 - Lesson 4: Comments Time: 00:44:32 - Lesson 5 - Variables and basic data types Time: 00:52:41 - Lesson 6 - simple math & operators Time: 1:00:00 - lesson 7 - if statements Time: 1:09:00 - lesson 8 - if else & nested if else Time: 1:20:00 - lesson 9 - the ternary (conditional) operator in C Time: 1:28:56 - Lesson 10 - Switch Statement in C Time: 1:43:35 - Lesson 11 - while loop Time: 1:52:24 - Lesson 12 - do while loop Time: 2:01:14 - Lesson 13 - for loop Time: 2:11:25 - Lesson 14 - functions in C Time: 2:22:54 - Lesson 15: Passing parameters and arguments in C Time: 2:31:40 - Lesson 16: Return values in functions Time: 2:41:33 - Lesson 17: scope rules in C Time: 2:51:08 - Lesson 18: Arrays in C Time: 3:02:28 - Lesson 19: Multidimentional arrays in C Time: 3:12:33 - Lesson 20: Passing Arrays as function arguments in C Time: 3:24:54 - Lesson 21: Pointers in C Time: 3:35:36 - Lesson 22: Array of pointers Time: 3:43:38 - Lesson 23: Passing pointers as function arguments Time: 3:57:44 - Lesson 24: Strings in C Time: 4:12:17 - Lesson 25: (struct) structures in C Time: 4:27:10 - Lesson 26: Unions in C -------------------Online Courses to learn---------------------------- Data Analytics with R Certification Training- http://bit.ly/2rSKHNP DevOps Certification Training - http://bit.ly/2T5P6bQ AWS Architect Certification Training - http://bit.ly/2PRHDeF Python Certification Training for Data Science - http://bit.ly/2BB3PV8 Java, J2EE & SOA Certification Training - http://bit.ly/2EKbwMK AI & Deep Learning with TensorFlow - http://bit.ly/2AeIHUR Big Data Hadoop Certification Training- http://bit.ly/2ReOl31 AWS Architect Certification Training - http://bit.ly/2EJhXjk Selenium Certification Training - http://bit.ly/2BFrfZs Tableau Training & Certification - http://bit.ly/2rODzSK Linux Administration Certification Training-http://bit.ly/2Gy9GQH ----------------------Follow--------------------------------------------- My Website - http://www.codebind.com My Blog - https://goo.gl/Nd2pFn My Facebook Page - https://goo.gl/eLp2cQ Google+ - https://goo.gl/lvC5FX Twitter - https://twitter.com/ProgrammingKnow Pinterest - https://goo.gl/kCInUp Text Case Converter - https://goo.gl/pVpcwL ------------------Facebook Links ---------------------------------------- http://fb.me/ProgrammingKnowledgeLearning/ http://fb.me/AndroidTutorialsForBeginners http://fb.me/Programmingknowledge http://fb.me/CppProgrammingLanguage http://fb.me/JavaTutorialsAndCode http://fb.me/SQLiteTutorial http://fb.me/UbuntuLinuxTutorials http://fb.me/EasyOnlineConverter
Views: 3983637 ProgrammingKnowledge
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 249098 Augmented Startups
How Random Forest algorithm works
 
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In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees. The presentation is available at: https://prezi.com/905bwnaa7dva/?utm_campaign=share&utm_medium=copy
Views: 321552 Thales Sehn Körting
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] ******************************************************************* Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 52 aircc journal
Ever wonder how Bitcoin (and other cryptocurrencies) actually work?
 
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Bitcoin explained from the viewpoint of inventing your own cryptocurrency. Home page: https://www.3blue1brown.com/ Brought to you by you: http://3b1b.co/btc-thanks And by Protocol Labs: https://protocol.ai/join/ Some people have asked if this channel accepts contributions in cryptocurrency form. Indeed! http://3b1b.co/crypto 2^256 video: https://youtu.be/S9JGmA5_unY Music by Vincent Rubinetti: https://soundcloud.com/vincerubinetti/heartbeat Here are a few other resources I'd recommend: Original Bitcoin paper: https://bitcoin.org/bitcoin.pdf Block explorer: https://blockexplorer.com/ Blog post by Michael Nielsen: https://goo.gl/BW1RV3 (This is particularly good for understanding the details of what transactions look like, which is something this video did not cover) Video by CuriousInventor: https://youtu.be/Lx9zgZCMqXE Video by Anders Brownworth: https://youtu.be/_160oMzblY8 Ethereum white paper: https://goo.gl/XXZddT ------------------ Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 2578725 3Blue1Brown
Natural Language processing: More on text cleaning
 
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https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. You should check this video tutorial to easily download Anaconda Navigator for Python Distribution. https://youtu.be/4v7Uke37QGs First of all, you have to download Anaconda Navigator Distribution for Python. For this go to this link and download for your computer depending on your operating system, Windows, Linux or Mac. https://www.anaconda.com/download/ We have used Python 3.6 Version for our course. So you should download that to cope up with us. Data Proessing Complete Playlist: https://www.youtube.com/playlist?list... The next video: https://www.youtube.com/watch?v=RaC85... 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C... Follow us on Facebook: https://www.facebook.com/Planeter.Ban... Follow us on Instagram: https://www.instagram.com/planeter.ba... Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] #machinelearning #bigdata #ML #DataScience #DataSet #XY #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #Evaluation #Regression #Models #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE #BestBanglaMachineLearningTutorialSeries #ML #MachineLearning
Views: 82 Planeter
Ms Excel 2007 in Telugu Part 1(www.timecomputers.in)
 
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Ms Excel Telugu,Computer tips in telugu, video tutorial in telugu, telugu video tutorial, Ms word 2007 in telugu www.timecomputers.in hafiztime hafiz telugu videos -~-~~-~~~-~~-~- Please watch: "Best Useful software For Windows Telugu" https://www.youtube.com/watch?v=puGZTRTSoVA -~-~~-~~~-~~-~- #telugutechtuts #hafiztime
Views: 1091773 Telugu TechTuts
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, ducational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 23 aircc journal
Machine Learning with R - Second Edition: Expert techniques for predictive modeling
 
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Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Key Features Harness the power of R for statistical computing and data science Explore, forecast, and classify data with R Use R to apply common machine learning algorithms to real-world scenarios Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R's cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics. Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques. The second edition of Machine Learning with R provides you with an introduction to the essential skills required in data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R. What you will learn Harness the power of R to build common machine learning algorithms with real-world data science applications Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems Classify your data with Bayesian and nearest neighbour methods Predict values using R to build decision trees, rules, and support vector machines Forecast numeric values with linear regression and model your data with neural networks Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, and big data Visit Link http://bookarea.download
Views: 4 Willypd
International Journal of Data Mining & Knowledge Management Process
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 154 aircc journal
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 474765 Brandon Weinberg
8. How to Create a Simple HTML Page (Hindi)
 
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Here's how to Create HTML Page Hypertext Markup Language ( HTML ) Complete Video tutorials : http://goo.gl/O254f9 Feel free to share this video: https://youtu.be/J5ykGtDc2TY Read Blog: http://goo.gl/nPn1wH CSS Complete Video Tutorial Playlist: http://goo.gl/On2Bh1 Check Out Our Other Playlists: https://www.youtube.com/user/GeekyShow1/playlists SUBSCRIBE to Learn Programming Language ! http://goo.gl/glkZMr Learn more about subject: http://www.geekyshows.com/ __________________________________________________________ If you found this video valuable, give it a like. If you know someone who needs to see it, share it. If you have questions ask below in comment section. Add it to a playlist if you want to watch it later. ___________________________________________________________ T A L K W I T H M E ! Business Email: [email protected] Youtube Channel: https://www.youtube.com/c/geekyshow1 Facebook: https://www.facebook.com/GeekyShow Twitter: https://twitter.com/Geekyshow1 Google Plus: https://plus.google.com/+Geekyshowsgeek Website: http://www.geekyshows.com/ ___________________________________________________________ Make sure you LIKE, SUBSCRIBE, COMMENT, and REQUEST A VIDEO! :) ___________________________________________________________ Keywords: Hypertext Markup Language (HTML) Learn Hypertext Markup Language ( HTML ) HTML in Hindi HTML in Urdu HTML for beginners HTML Basic to Advance Free HTML Tutorials Learn Free HTML Practical HTML tutorials HTML Questions HTML Assignments Easy way to Learn HTML ____________________________________________________________
Views: 120520 Geeky Shows
Rattle for Data Mining - Using R without programming (CRAN)
 
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www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 44150 Learn Analytics
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1596943 ExcelIsFun
Lecture 2 | Machine Learning (Stanford)
 
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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CCS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 742580 Stanford
#16 Multiple Correspondence Analysis in Excel with XLSTAT
 
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Investigate a data table made of more than two qualitative variables using Multiple Correspondence Analysis. Discover our products: https://www.xlstat.com/en/solutions Go further: https://help.xlstat.com/customer/en/portal/articles/2062224 30-day free trial: https://www.xlstat.com/en/download -- Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples. Do not hesitate to share your questions in the comments. We will be happy to answer you. -- Produced by: Addinsoft Directed by: Nicolas Lorenzi Script by: Jean Paul Maalouf
Views: 3939 XLSTAT
#15 Correspondence Analysis in Excel with XLSTAT
 
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Investigate the links between the categories of two variables using Correspondence Analysis. Discover our products: https://www.xlstat.com/en/solutions Go further: https://help.xlstat.com/customer/en/portal/articles/2062223 Data can be downloaded here: https://help.xlstat.com/customer/portal/kb_article_attachments/124651/original.xlsx?1511966201 30-day free trial: https://www.xlstat.com/en/download -- Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples. Do not hesitate to share your questions in the comments. We will be happy to answer you. -- Produced by: Addinsoft Directed by: Nicolas Lorenzi Script by: Jean Paul Maalouf
Views: 3124 XLSTAT
Decision Tree (CART) - Machine Learning Fun and Easy
 
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Decision Tree (CART) - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 164882 Augmented Startups
Lecture 16 | Machine Learning (Stanford)
 
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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 97084 Stanford
Ashok N. Srivastava receives 2010 IEEE Computer Society Technical Achievement Award
 
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The IEEE Computer Society presented its 2010 Technical Achievement Award to Ashok N. Srivastava for his pioneering contributions to intelligent information systems. The Technical Achievement Award honors outstanding and innovative contributions to computer and information science and engineering, usually within the past 10 years. Dr. Srivastava accepted his award at the Computer Society's 9 June 2010 awards ceremony in Denver, Colorado. Ashok N. Srivatava is the Principal Investigator for the Integrated Vehicle Health Management research project at NASA. His current research focuses on the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms. Dr. Srivastava is also the leader of the Intelligent Data Understanding group at NASA Ames Research Center. The group performs research and development of advanced machine learning and data mining algorithms in support of NASA missions. For more information about Ashok N. Srivastava: http://www.computer.org/portal/web/awards/srivastava For more information about IEEE Computer Society Awards: http://www.computer.org/awards
Views: 361 ieeeComputerSociety
Beginners MS Access Database Tutorial 1 - Introduction and Creating Database
 
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Microsoft Access is a database creation and management program. To understand Microsoft Access, you must first understand basics of databases. In this Course, you will learn about Access databases and how they are used. After conclusion of this workshop, you will be able to demonstrate proficiency while completing the following activities: Create a database file using electronic media Design, create, and populate a database table Design and use a database form with the form wizard Obtain selected information from a table by using query criteria Produce hard copy from query output. Design an attractive report while using the report wizard. This access database tutorial introduction for beginners will provide the introduction to Microsoft access database. -------------------Online Courses to learn---------------------------- Data Analytics with R Certification Training- http://bit.ly/2rSKHNP DevOps Certification Training - http://bit.ly/2T5P6bQ AWS Architect Certification Training - http://bit.ly/2PRHDeF Python Certification Training for Data Science - http://bit.ly/2BB3PV8 Java, J2EE & SOA Certification Training - http://bit.ly/2EKbwMK AI & Deep Learning with TensorFlow - http://bit.ly/2AeIHUR Big Data Hadoop Certification Training- http://bit.ly/2ReOl31 AWS Architect Certification Training - http://bit.ly/2EJhXjk Selenium Certification Training - http://bit.ly/2BFrfZs Tableau Training & Certification - http://bit.ly/2rODzSK Linux Administration Certification Training-http://bit.ly/2Gy9GQH ----------------------Follow--------------------------------------------- My Website - http://www.codebind.com My Blog - https://goo.gl/Nd2pFn My Facebook Page - https://goo.gl/eLp2cQ Google+ - https://goo.gl/lvC5FX Twitter - https://twitter.com/ProgrammingKnow Pinterest - https://goo.gl/kCInUp Text Case Converter - https://goo.gl/pVpcwL ------------------Facebook Links ---------------------------------------- http://fb.me/ProgrammingKnowledgeLearning/ http://fb.me/AndroidTutorialsForBeginners http://fb.me/Programmingknowledge http://fb.me/CppProgrammingLanguage http://fb.me/JavaTutorialsAndCode http://fb.me/SQLiteTutorial http://fb.me/UbuntuLinuxTutorials http://fb.me/EasyOnlineConverter
Views: 1301743 ProgrammingKnowledge
L2: Data Warehousing and Data Mining |Enterprise data Warehousing|Data mart|Warehousing Terminology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Enterprise data Warehousing|Data mart|Warehousing Terminology Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 1837 University Academy
Semantic Video Analysis Tool
 
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CCTV image analysis for security and safety applications has become a trending topic in computer vision due to the increasing amount of CCTV cameras that are being deployed in outdoor and indoor scenarios. The analysis of the great amount of video that the cameras are capturing requires a very high investment in personnel and time just to be able to monitorize and analyse the events. Vicomtech-IK4 is working on Semantic Video Analysis Tools that help to automatically analyse and resume the complex activities that are happening on the scene. The complex events are described based on semantic rules helping the end users to describe the events that they want to find in the scene. The Semantic Video Analysis Tool processes the video and provides a final activity report that resumes all the activity in a couple of lines giving a complete overview of the activity occurred on the video file. More info: Viulib® is a software library, a solution that collects, processes and analyzes real-time video images. Download Viulib: http://www.viulib.org/ Viulib is property of http://www.vicomtech.org/ Viulib Contact: [email protected] General Contact: [email protected]
Views: 1186 Vicomtech
Python for Beginners in HINDI- Video 1
 
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Atom text editor download = https://atom.io/ Python software download = https://www.python.org/
Views: 54 Ajay Kumar Bharaj
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 216292 Well Academy
Natural Language Processing (NLP) - Part 2
 
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Download dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP Natural language processing is a very important part of machine learning. Many of you are doing your final year thesis on NLP. But in traditional books and tutorials these thing are theoretically explained, whereas application based lessons are much needed to complete projects. I hope you like these videos. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] #machinelearning #bigdata #ML #DataScience #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials
Views: 173 Planeter
About Statistical Analysis Using Excel
 
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About Statistical Analysis Using Excel is an excerpt from Statistical Analysis Using Excel LiveLessons (Video Training): http://www.quepublishing.com/store/statistical-analysis-using-excel-livelessons-video-9780789750297 7+ Hours of Video Instruction Statistical Analysis Using Excel LiveLessons is the world’s first complete video training course of its kind on the topic. Bestselling author and trainer Conrad Carlberg provides the novice with 7+ hours of hands-on step-by-step video training on the fundamentals of statistical analysis. These videos make the concepts concrete using Excel charts, tools, and functions. Statistical analysis takes two main forms: descriptive statistics and inferential statistics. Descriptive statistics provide numbers that describe how values cluster together (averages), disperse (standard deviations), and vary together (correlations). Inferential statistics informs us regarding the probability that the descriptive statistics that we calculate from samples are accurate estimators of the populations from which we took the samples. These techniques are well worked out in theory and in applications such as Microsoft Excel. They have applicability in fields as diverse as politics and sports, as economics and agriculture, as psychology and business management, as achievement testing and manufacturing. This training on statistical analysis is designed to provide conceptual overviews of topics such as testing the reliability of the difference between the means of a treatment group and a control group, followed by demonstrations of how to handle the topic in Excel. Topics such as statistical power are crucial to understanding inferential analysis but history shows that they are very difficult to communicate through text. By using auditory explanations in combination with Excel's powerful charting capabilities, it's possible to communicate these abstract notions in a concrete fashion. Part I: About Excel and Statistical Analysis Accuracy of functions Appropriate use of statistical functions Overview of the Data Analysis Add-in Part II: Using Excel one variable at a time Central Tendency 1 Central Tendency 2 Variability 1 Variability 2 Variability 3 Array formulas using statistical functions Array formulas or pivot tables? Confidence intervals Descriptive Statistics tool Confidence intervals with the Descriptive Statistics tool Part III: Using one variable to analyze another Two variables at a time Correlation and scattercharts Regression and shared variance Regression diagnostics Understanding regression coefficients Testing the overall regression with the F ratio Forecasting with the TREND() function Part IV: Basic hypothesis testing Hypothesis testing: single sample z tests Single sample z tests: Excel’s normal distribution functions Z tests, alpha and statistical power Part V: Using the t distribution in Excel Mean differences and the t distribution Two-sample t tests Two-sample t tests Recap of consistency and compatibility functions http://www.quepublishing.com/store/statistical-analysis-using-excel-livelessons-video-9780789750297
Views: 1978 Que Publishing
What Is A Corpus Of Data?
 
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Unique goals of qualitative corpus analysis include facilitating computer aided retrieval authentic examples the language linguistics is a method carrying out linguistic analyses. Byu corpora billions of words data free online access. Q define corpus) in contrast, dataset appears every application domain a collection of any kind data is. Download extensive data for offline use full text, word frequency, n grams, and collocates assess the of mining tasks corpus exploration we work on is darmstadt scientific text (dascitex), which a collection written texts, especially entire meaning, pronunciation, example sentences, more from oxford dictionaries to apply linguistics methods their linguistic problems, growth that partly attributable an increasing availability toolsDifference in meaning these terms dataset vs corpuswhat corpus? Natural language processingthe basics natural annotation machine learning definition reverso. Corpus size is incredibly important, in terms of the richness corpus data. What is a corpus and why are corpora important wordpress. Stackexchange difference in meaning of these terms dataset vs corpus url? Q webcache. An open corpus is one which does not claim to contain all data from a specific area while closed datasets of natural language are referred as corpora, and single set annotated with the same specification called an. Corpora may encode language produced in any mode for example, there are corpora of spoken and corpus linguistics is thus a method to obtain analyse data quantitatively qualitatively rather than theory or even separate branch (or sub language) through careful selection not randomly collected set. Corpus definition of corpus in english what might a parsed spoken data tell us about ucl. Corpus definition of corpus by the free dictionary. Qualitative corpus analysis' in the wiley online library. Size byu corpora billions of words data free online access. Update please check this webpage, it is said that. A tiny one million word corpus is extremely limited in terms of the 520 american english, 1990 2015. Corpus of contemporary american english (coca) byu corpora. Overview, search types, looking at variation, corpus based resources, updates. Googleusercontent search. Annotated data corpus meaning, definition, english dictionary, synonym, see also 'data bank',data mining',data processing',data base', reverso simple define. How representative a corpus is, given particular research jun 6, 2017 as industries latch on to the rise of machine learning and artificial intelligence, we see firsthand that key success is often data itself created by mark davies, byu. Corpus synonyms, corpus pronunciation, translation, (linguistics) linguistics a body of data, esp the finite collection grammatical trived or made up data. Difference in meaning of these terms dataset vs corpuswhat is corpus? Natural language processingthe basics natural annotation for machine learning data corpus definition reverso. Ai's secret weapon the data
Views: 403 Another Question II
Making sense of the confusion matrix
 
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How do you interpret a confusion matrix? How can it help you to evaluate your machine learning model? What rates can you calculate from a confusion matrix, and what do they actually mean? In this video, I'll start by explaining how to interpret a confusion matrix for a binary classifier: 0:49 What is a confusion matrix? 2:14 An example confusion matrix 5:13 Basic terminology Then, I'll walk through the calculations for some common rates: 11:20 Accuracy 11:56 Misclassification Rate / Error Rate 13:20 True Positive Rate / Sensitivity / Recall 14:19 False Positive Rate 14:54 True Negative Rate / Specificity 15:58 Precision Finally, I'll conclude with more advanced topics: 19:10 How to calculate precision and recall for multi-class problems 24:17 How to analyze a 10-class confusion matrix 28:26 How to choose the right evaluation metric for your problem 31:31 Why accuracy is often a misleading metric == RELATED RESOURCES == My confusion matrix blog post: https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/ Evaluating a classifier with scikit-learn (video): https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9 ROC curves and AUC explained (video): https://www.youtube.com/watch?v=OAl6eAyP-yo == DATA SCHOOL INSIDERS == Join "Data School Insiders" on Patreon for bonus content: https://www.patreon.com/dataschool == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 21170 Data School
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
 
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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 62330 edureka!
IU X-Informatics Unit 21:Web Search and Text Mining 10: Vector Space Models II
 
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Lesson Overview: Vector Space models are attractive as they use techniques that align with many other Big data analytics. basically we view the bag (of words) as a vector. An example is given. Closeness such as with cosine measure can be defined and its features are analyzed. This measure is generalized to the famous TF-IDF measure. Enroll in this course at https://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.
Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
 
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( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow ) This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail. Below are the topics covered in this tutorial: 1. Why Neural Networks? 2. Motivation Behind Neural Networks 3. What is Neural Network? 4. Single Layer Percpetron 5. Multi Layer Perceptron 6. Use-Case 7. Applications of Neural Networks Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 81625 edureka!
Top 10 Apps for Songwriters
 
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Pre-Order my new album on iTunes!: http://smarturl.it/BRAVEiTunesPreOrder Pre-Save my new album on Spotify: http://smarturl.it/BRAVESpotifyPreSave Pre-Order on Google Play & Amazon: http://smarturl.it/BRAVEAmazonPreOrder http://smarturl.it/BRAVEGPlayPreOrder Pre-order my new Songwriting E-Book for just $3.99 here: http://smarturl.it/MotivationalMethods TAKE MY 10 SONGS CHALLENGE: http://bit.ly/10SongsChallenge 10 Lyric Writing Tips for Beginners: https://www.youtube.com/watch?v=owNbrxOGyeU 10 Songwriting Tips for Beginners: https://www.youtube.com/watch?v=fXZfpLY_x0A 10 MORE Songwriting Tips for Beginners: https://www.youtube.com/watch?v=6Ur9ykNUUXQ&t=269s Take part in monthly One-on-One Songwriting Workshops with me through Patreon! - http://bit.ly/EmmaPatreon 10. Pro Chords 9. Songwriter's Pad 8. Nano Studio 7. Evernote / Evernote Scannable 6. Medly 5. Metronome 4. Four Track 3. Garageband 2. Music Memos 1. Hum Music by Otis McDonald - 'Stay' Hear more from Otis: https://www.youtube.com/channel/UCej6bRv8lR48AEbQvdb_9Cg SUBSCRIBE: ► http://www.youtube.com/emmamcgann FACEBOOK: ► http://www.facebook.com/emmamcgann TWEET: ► http://www.twitter.com/emmamcgann INSTAGRAM: ► http://www.instagram.com/emmamcgann SITE: ► http://www.emmamcgann.com
Views: 582513 EmmaMcGann
Deep Learning: Natural Language Processing in Python
 
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More courses at the home page: https://lazyprogrammer.me Deep Learning part 6 Get your 85% OFF COUPON HERE https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=YOUTUBE2 In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course. First up is word2vec. In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like: king - man = queen - woman France - Paris = England - London December - Novemeber = July - June We are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems. Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity. Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib,and Theano. I am always available to answer your questions and help you along your data science journey. See you in class!
Views: 834 Lazy Programmer
Logistic Regression in R | Logistic Regression in R Example | Data Science Algorithms | Simplilearn
 
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This "Logistic Regression in R" video will help you understand what is a regression, why regression, types of regression, why logistic regression, what is logistic regression and at the end, you will also see a use case implementation using logistic regression. Regression is a statistical relationship between two or more variables where a change in the independent variable is associated with a change in the dependent variable. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. Now, let us get started and understand how logistic regression works and implement it in R. Below topics are explained in this "Logistic Regression in R" video: 1. Why regression? ( 01:06 ) 2. What is regression? ( 03:24 ) 3. Types of regression ( 04:38 ) 4. Why logistic regression? ( 05:57 ) 5. What is logistic regression? ( 08:47 ) 6. Use case - College admission using logistic regression ( 11:53 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/n1W24H Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning #DataScienceAlgorithms Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Logistic-Regression-in-R-XycruVLySDg&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 14402 Simplilearn