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Statistics for Data Science 2018 Part 1 | Statistics Tutorial For Beginners | Data Science Tutorial
 
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Statistics for Data Science 2018 Part 1 | Statistics Tutorial for Beginners | Data Science Tutorial https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=c27EwKNIanQ&campaign=youtube_channel&utm_source=youtube&utm_medium=statistics-tut-sumit-part-1&utm_campaign=youtube_channel Hello and Welcome to one of the Best Data Science tutorial conducted by Acadgild. This video talks about applications of statistics for data science. Let’s check the topics covered in this tutorial. After completing this training session, you will be able to learn, • Introduction to Statistics • Basic Statistics • Introduction to the Basic Terms of Statistics • What are Variables • The Measure of Central Tendency the Mean, Median, and Mode • The Measures of Dispersion • What is a Range • What is Sample Variance • Standard Deviation • Population Vs Sample • What is Chebysheff’s Theorem • Law of Expected Values and Variance • Probability Density Function Check out the basic terms used in statistics: Variable, Data (singular), Data (plural), Experiment, Parameter, Statistics. Kinds of Variables or Types of Variable: Qualitative or Attributive or Categorical variable Kindly, go through the complete video and learn more about statistics and please subscribe the channel for more updates on the latest technical skills and tutorials. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 41160 ACADGILD
Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ 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: 93697 Siraj Raval
DATA SCIENCE FREE BOOKS ONLINE | DOWNLOAD YOUR COPY
 
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Here's a list of 10 must read book on Data Science & Machine Learning. Foundations of DATA SCIENCE Book www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning Book www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf The Elements of Statistical Learning Book web.stanford.edu/~hastie/Papers/ESLII.pdf An Introduction to Statistical Learning Book www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf Mining of Massive Data Sets Book infolab.stanford.edu/~ullman/mmds/book.pdf
Views: 1914 DATA SCIENCE
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 417688 CrashCourse
Learn Data Science in 3 Months
 
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I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 240211 Siraj Raval
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 969646 David Langer
Hypothesis testing with Applications in Data Science
 
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In this video, We talk about a quintessential statistics topic you need to know to know as a data scientist: hypothesis testing. We’ll take a look a description of a hypothesis test and see how we can use this in real applications. If you like the video hit that like button. If you’re new here, welcome. And hit that SUBSCRIBE button. FUN LINKS Concepts on Hypothesis Testing: https://onlinecourses.science.psu.edu/statprogram/reviews/statistical-concepts/hypothesis-testing Non-equidistant data ANOVA: file:///Users/Ajay/Downloads/ejbrm-volume12-issue1-article336.pdf ANOVA on non-normal data: http://www.psicothema.com/pdf/4434.pdf FOLLOW ME Quora : https://www.quora.com/profile/Ajay-Halthor
Views: 878 CodeEmporium
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. 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. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 69951 edureka!
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 741444 statisticsfun
Best Machine Learning Books
 
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Best machine learning books - these are the best machine learning books in my opinion. If you fancy buying me a coffee, you can do it here :-) https://ko-fi.com/pythonprogrammer Amazon (affiliate links) Hello World - Being Human in the Age of Algorithms https://amzn.to/2qJUjds Machine Learning for absolute beginners https://amzn.to/2K4dur4 Introduction to Statistical Learning https://amzn.to/2PwBI3o (Free version https://www-bcf.usc.edu/~gareth/ISL/ ) A First Course in Machine Learning https://amzn.to/2DlRqae The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://amzn.to/2DHexwG - (Free version http://www-stat.stanford.edu/~tibs/ElemStatLearn/ ) Artificial Intelligence: A Modern Approach https://amzn.to/2PYzbOB Machine Learning: A Probabilistic Perspective https://amzn.to/2DqtEtQ Pattern Recognition and Machine Learning https://amzn.to/2DGtT4P Information Theory, Inference and Learning Algorithms https://amzn.to/2RX7nrd (Free version http://www.inference.org.uk/itprnn/book.pdf ) Make your Own Neural Network https://amzn.to/2DGG5T1 The Master Algorithm https://amzn.to/2DGgfia
Views: 14718 Python Programmer
Linear Regression Algorithm | Linear Regression in Python | Machine Learning Algorithm | Edureka
 
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** Machine Learning Training with Python: https://www.edureka.co/python ** This Linear Regression Algorithm video is designed in a way that you learn about the algorithm in depth. This video is designed in a way that in the first part you will learn about the algorithm from scratch with its mathematical implementation, then you will drill down to the coding part and implement linear regression using python. Below are the topics covered in this tutorial: 1. What is Regression? 2. Regression Use-case 3. Types of Regression – Linear vs Logistic Regression 4. What is Linear Regression? 5. Finding best-fit regression line using Least Square Method 6. Checking goodness of fit using R squared Method 7. Implementation of Linear Regression Algorithm using Python (from scratch) 8. Implementation of Linear Regression Algorithm using Python (scikit lib) Check out our playlist for more videos: http://bit.ly/2taym8X Subscribe to our channel to get video updates. Hit the subscribe button above. #LinearRegressionAlgorithm #LinearRegressionAlgorithmUsingPython #LeastSquareMethod #RsquareMethod 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 Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. 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
Views: 78604 edureka!
What is Linear Regression?
 
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What is Linear Regression? In this video I explain what linear regression is, why it’s used and briefly show you how to implement it in Python using scikit learn and statmodels. I also explain R squared, t-value and p-value. More information and learning resources on Linear Regression If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer Introduction to Statistical Learning - http://bit.ly/2ELFL6Z (Free PDF of Book) #linearregression #linearregressionpython #machinelearningalgorithms
Views: 2049 Python Programmer
Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn
 
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This Machine Learning Interview Questions And Answers video will help you prepare for Data Science and Machine learning interviews. This video is ideal for both beginners as well as professionals who are appearing for Machine Learning or Data Science interviews. Learn what are the most important Machine Learning interview questions and answers and know what will set you apart in the interview process. Some of the important Machine Learning Interview Questions are listed below: 1. What are the different types of Machine Learning? 2. What is overfitting? And how can you avoid it? 3. What is false positive and false negative and how are they significant? 4. What are the three stages to build a model in Machine Learning? 5. What is Deep Learning? 6. What are the differences between Machine Learning and Deep Learning? 7. What are the applications of supervised Machine Learning in modern businesses? 8. What is semi-supervised Machine Learning? 9. What are the unsupervised Machine Learning techniques? 10. What is the difference between supervised and unsupervised Machine Learning? 11. What is the difference between inductive Machine Learning and deductive Machine Learning? 12. What is 'naive' in the Naive Bayes classifier? 13. What are Support Vector Machines? 14. How is Amazon able to recommend other things to buy? How does it work? 15. When will you use classification over regression? 16. How will you design an email spam filter? 17. What is Random Forest? 18. What is bias and variance in a Machine Learning model? 19. What’s the trade-off between bias and variance? 20. What is pruning in decision trees and how is it done? Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube You can also go through the Slides here: https://goo.gl/rmzjaQ #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence - - - - - - For more updates on courses and tips follow us on: - 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: 42895 Simplilearn
SAS Visual Data Mining and Machine Learning
 
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http://www.sas.com/vdmml Boost analytical productivity and solve your most complex problems faster with a single, integrated in-memory environment that's both open and scalable. SAS VISUAL DATA MINING AND MACHINE LEARNING SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine-learning process with a comprehensive, visual (and programming) interface that handles all tasks in the analytical life cycle. It suits a variety of users and there is no application switching. From data management to model development and deployment, everyone works in the same, integrated environment. http://www.sas.com/vdmml SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 5172 SAS Software
Naïve Bayes Classifier -  Fun and Easy Machine Learning
 
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Naive Bayes Classifier- 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 -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ 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: 136549 Augmented Startups
Top 10 Must Free Resource for Data Science and Machine Learning
 
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Must Read Books to kick start in AI | Machine Learning | Data Science Resource Links : Machine Learning Yearning By Andrew Ng http://www.mlyearning.org/ Think Stats: Probability and Statistics for Programmers by Allen Downey http://www.greenteapress.com/thinkstats/ Foundations of Data Science https://www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning: From Theory to Algorithms http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/ GitHub - https://github.com/jakevdp/PythonDataScienceHandbook The Elements of Statistical Learning https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print10.pdf Natural Language Processing with Python https://www.nltk.org/book/ Probabilistic Programming & Bayesian Methods for Hackers http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ GitHub - https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Deep Learning http://www.deeplearningbook.org/ A Programmer's Guide to Data Mining http://guidetodatamining.com/ An Introduction to Statistical Learning Video Lectures - https://www.alsharif.info/iom530 Book - http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf Machine Learning and Big Data http://www.kareemalkaseer.com/books/ml/word-of-intro Thank you and Happy Learning
Views: 443 Pinku Ki Pathshala
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algorithms | Simplilearn
 
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This Machine Learning Algorithms Tutorial video will help you learn you what is Machine Learning, various Machine Learning problems and the algorithms, key Machine Learning algorithms with simple examples and use cases implemented in Python. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest and KNN algorithm. This Machine Learning Algorithms tutorial is designed for beginners to understand which algorithm to use when, how each algorithm works and implement it on Python with real-life use cases. Below topics are covered in this Machine Learning Algorithms Tutorial: 1. Real world applications of Machine Learning 2. What is Machine Learning? 3. Processes involved in Machine Learning 4. Type of Machine Learning Algorithms 5. Popular Algorithms with hands-on demo - Linear regression - Logistic regression - Decision tree and Random forest - N Nearest neighbor What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-Algorithms-I7NrVwm3apg&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Algorithms-I7NrVwm3apg&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - 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: 43532 Simplilearn
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
01:33:00
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 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 Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course 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: 471875 edureka!
What is data science In telugu  - డేటా సైన్స్ అంటే ఏమిటి -9059868766 Artificial intelligence AI Demo
 
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data science training python videos, datacamp data science python, intro to python for data science course by datacamp, python data science course, python data science tutorial, python for data science book, python for data science pdf, python training videos, youtube python data science, What is data science In telugu - డేటా సైన్స్ అంటే ఏమిటి Download data science content Pdf https://goo.gl/JN6iGs http://www.sivaitsoft.com/data-science-online-training-kukatpally/ What is data science course? What is a data scientist? Who coined data science? What is big data analysis? Data Science course content vlrtraining 9059868766 Hyderabad https://goo.gl/JN6iGs DATA SCIENCE ONLINE TRAINING Data Science Online Training kukatpally Hyderabad provided by VLR Trainings. Data Science is that the study ofDATA SCIENCE Online training wherever data comes from, what it represents and the way it is became a valuable resource in the creation of business and IT ways. More info Wikipedia DATA SCIENTIST A data scientist is someone who is better at statistics than any software engineer and better at Software engineering than any statistician.” WHAT A DATA SCIENTIST DOES Most data scientists in the industry have advanced degrees and training in statistics, math, and computer science. Their experience is a vast horizon that also extends to data visualization, data mining, and information management. It is fairly common for them to have previous experience in infrastructure design, cloud computing, and data warehousing. SKILLS REQUIRED TO BECOME A DATA SCIENTIST Statistic and probability Algorithms Programming Languages (Java, Scala ,SQL, R, Phyton) Data mining Machine learning Who should go for this course? Fresher’s/Graduates Job Seekers Managers Data analysts Business analysts Operators End users Developers IT professionals Data science Course Duration and details Course Duration 90Days (3 months) Course Fees 27000Rs Only online training Note* Everyday session recordings are also available Venkat: 9059868766 Jio:7013158918 Email: [email protected] Address: Vlrtraining/Sivaitsoft PlotNo 126/b,2nd floor,Street Number 4, Addagutta Society, Jal Vayu Vihar,, Kukatpally, Hyderabad, Telangana 500085 Map Link https://goo.gl/maps/Nk9LziFjVXS2 Data science Course Content data science, data science and analytics, data science certification, data science course, data science degree, data science online, data science pdf,, data science skills, data science syllabus, data science tools, data scientist profile, data scientist skills, introduction to data science, learn data science, mathematics for data science, python data science, science data, scientific database, Download Pdf Data Science course content vlrtraining 9059868766 Hyderabad http://www.sivaitsoft.com/wp-content/uploads/2017/10/Data-Science-course-content-vlrtraining-9059868766-Hyderabad.pdf
Views: 23623 VLR Training
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 165922 Well Academy
ROC Curves and Area Under the Curve (AUC) Explained
 
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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf 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: 298646 Data School
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 111138 LearnEveryone
Introduction to Machine Learning with MATLAB!
 
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Get The Complete MATLAB Course Bundle for 1 on 1 help! https://josephdelgadillo.com/product/matlab-course-bundle/ Enroll in the FREE course! https://uthena.com/courses/matlab?coupon=youtube&ref=744aff This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The course consists of the following sections: Segment 1: Instructor and Course Introduction Segment 2: MATLAB Crash Course Segment 3: Grabbing and Importing Data-set Segment 4: K-Nearest Neighbor Segment 5: Naive Bayes Segment 6: Decision Trees Segment 7: Discriminant Analysis Segment 8: Support Vector Machines Segment 9: Error Correcting Output Codes Segment 10: Classification with Ensembles Segment 11: Validation Methods Segment 12: Evaluating Performance As bonus, you also learn how to share your analysis results with your colleges, friends, and others, and create visual analysis of your results. You will also have access to some practice questions which will give you hands on experience. Time Stamps: 01:29 Introduction 03:04 Why MATLAB for machine learning 06:16 Meet the instructor, Dr. Nouman Azam 09:25 MATLAB crash course 19:30 Applications of machine learning 31:28 Data types you will encounter 39:55 Importing data into MATLAB 49:36 Data tables Web - https://josephdelgadillo.com Subscribe - https://bit.ly/SubscribeJTD Facebook - https://www.facebook.com/delgadillojt Discord - https://discord.gg/EbcQFrg Instagram - https://www.instagram.com/jo3potato
Views: 28586 Joseph Delgadillo
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
01:10:56
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 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 Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course 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: 355164 edureka!
Data Analysis with Python for Excel Users
 
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A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Views: 173508 APMonitor.com
Statistics Tutorial for Data Science 2018 Part-1| Introduction to Statistics | Data Science Tutorial
 
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Statistics Tutorial for Data Science | Introduction to Statistics | Data Science Tutorial https://acadgild.com/big-data/deep-learning-course-training-certification?utm_campaign=enrol-statistics-tutorial-FmWz4deqo9g_medium=VM&utm_source=youtube Hello and Welcome to Statistics tutorial for Data Science conducted by Acadgild. This Data Scientist tutorial will help you learn, how to become a Data scientist and how to master the data science. As you all know this was an online data science tutorial for statistics driven by experts and it’s not a live session for your knowledge. Let’s understand, what this video talks about: • What is Statistics • Introduction to Basic Terms of Statistics • Variables • Mean, Median and Mode • Measure of Dispersion • Range • Sample Variance • Standard Deviation • Population Vs. Sample • Chebyshev's Theorem • Law of Expected Values and Variance • Probability Density Function Watch the entire video and Become a Data Scientist. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 23896 ACADGILD
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 378872 APMonitor.com
R-Session 9 - Statistical Learning - Support Vector Machines
 
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Reference: (Book) An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani) http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf Reference (Lecture Notes) [1] With permission from Dr. Tibshirani and Dr. Hastie, the Lecture notes are adopted from Stanford-Online StatLearning Statistical Learning [2] With permission from Dr. Al Sharif (USC) part of the Lecture notes were adopted from "DSO 530: Applied Modern Statistical Learning Techniques".
Views: 34799 Hamed Hasheminia
Data Mining with Weka (4.2: Linear regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 42478 WekaMOOC
Data Mining with Weka (4.4: Logistic regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 32786 WekaMOOC
Data Science & Machine Learning -Creating a Shiny App- DIY- 43 -of-50
 
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Data Science & Machine Learning -Creating a Shiny App- DIY- 43 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Deploying you R application with Shiny Shiny is an open source R package, which combines the computational power of R with the interactivity of the modern web. It provides a powerful web framework for building web applications using R.  Shiny helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge. Enables standalone apps on a webpage or embed them in R Markdown documents or build dashboards. Shiny Code Components To use Shiny, initiate library. There are two components of the code – ui and server. You can create a single app.R with ui and server codes or you separately in ui.R and Server.R Hands On – R Machine Learning Ex-19 Create a basic structure of a Shiny app. Goto https://shiny.rstudio.com/ and click on getting started – read more about Shiny apps. Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes, Apriori, Shiny
Views: 4188 BharatiDWConsultancy
Data Mining with Weka (2.5: Cross-validation)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Cross-validation http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 40282 WekaMOOC
Interpretable Machine Learning Meetup
 
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This meetup was recorded in New York City on September 10th, 2018. Slides from the meetup can be found here: https://github.com/jphall663/jsm_2018_slides/blob/master/main.pdf Description: The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models Speaker's Bio: Patrick Hall is a senior director for data science products at H2o.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2o.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick was the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Views: 680 H2O.ai
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. 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. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies 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 Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."
Views: 46834 edureka!
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1415 WekaMOOC
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - 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
Views: 63415 edureka!
Advanced Data Mining with Weka (3.3: Using R to plot data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Using R to plot data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3905 WekaMOOC
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68558 WekaMOOC
How Do Algorithms Predict Criminal Behavior?
 
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Host Myles Bess takes a deeper look at how algorithms are being used in the criminal justice system -- and whether not computer programs make better predictions about criminal behavior than humans. SUBSCRIBE to Above the Noise: [https://www.youtube.com/abovethenoise?sub_confirmation=1] ABOVE THE NOISE is a show that cuts through the hype and takes a deeper look at the research behind controversial and trending topics in the news. Hosted by Myles Bess and Shirin Ghaffary. *NEW VIDEOS EVERY OTHER WEDNESDAY* It’s no big secret that the United States has a prison problem. We lock up people at higher rates than any other nation, and there are huge racial disparities in who we lock up. According to a study from The Sentencing Project, in state prisons, African Americans are incarcerated 5 times more than whites. There are lots of reasons for why we may see this racial disparities, including law enforcement practices, crime rates, and punitive sentencing policies. Keeping so many people in prison is really expensive-- it costs about $80 billion dollars a year-- and it contributes to racial inequalities in America. As a result, there’s a big push among both Democrats and Republicans to reform our prison system. And one popular strategy many people advocate for as part of this reform effort are risk assessment tools. The tools use data to predict whether a person will commit a future crime. This video explores how these tools work and some of the controversy surrounding their use. What do you think does a better job of predicting someone’s risk of committing a future crime -- data-driven algorithms or human judgement? Check out some of these tools: Sample of COMPAS Tool (Obtained by ProPublica): https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment-COMPAS-CORE.html http://www.northpointeinc.com/downloads/research/DCJS_OPCA_COMPAS_Probation_Validity.pdf (Appendix A: page 32) Indiana Risk Assessment System Tools: http://www.pretrial.org/download/risk-assessment/Indiana%20Risk%20Assessment%20System%20(April%202010).pdf Missouri Tool: http://www.mosac.mo.gov/page.jsp?id=45500 Virginia: (Appendix D, page 132) http://www.vcsc.virginia.gov/risk_off_rpt.pdf In-depth article on this topic: https://ww2.kqed.org/lowdown/2017/06/01/can-algorithms-predict-criminal-behavior/ Sources: Risk Assessment in Criminal Sentencing https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2662082# Risk Assessment Instruments Validated and Implemented in Correctional Settings in the United States: An Empirical Guide https://csgjusticecenter.org/wp-content/uploads/2014/07/Risk-Assessment-Instruments-Validated-and-Implemented-in-Correctional-Settings-in-the-United-States.pdf Risk Needs Assessment in the Criminal Justice System https://fas.org/sgp/crs/misc/R44087.pdf Bureau of Justice Statistics https://www.bjs.gov/index.cfm?ty=pbdetail&iid=5177 Incarceration gap widens between whites and blacks http://www.pewresearch.org/fact-tank/2013/09/06/incarceration-gap-between-whites-and-blacks-widens/ Marijuana Arrests by the Numbers https://www.aclu.org/gallery/marijuana-arrests-numbers Mass Incarceration: The Whole Pie https://www.prisonpolicy.org/reports/pie2016.html Follow KQED: KQED: http://www.kqed.org/ Facebook: https://www.facebook.com/KQED/ Twitter: https://twitter.com/KQED?lang=en Teachers follow KQED Learning KQED Learning: https://ww2.kqed.org/learning/ Facebook: https://www.facebook.com/KQEDLearning/ Twitter: https://twitter.com/KQEDedspace?lang=en About KQED KQED, an NPR and PBS affiliate in San Francisco, CA, serves Northern California and beyond with a public-supported alternative to commercial TV, Radio and web media. Funding for Above the Noise is provided in part by S.D. Bechtel, Jr. Foundation, David Bulfer and Kelly Pope, Horace W. Goldsmith Foundation, The Dirk and Charlene Kabcenell Foundation, The Koret Foundation, Gordon and Betty Moore Foundation, Smart Family Foundation, The Vadasz Family Foundation and the members of KQED.
Views: 13413 Above The Noise
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Ensembles (3): Gradient Boosting
 
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Gradient boosting ensemble technique for regression
Views: 98563 Alexander Ihler
Distribution Analysis Using SAS Studio
 
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In this video, you learn how to use the Distribution Analysis task in SAS Studio. You learn how to request histograms with overlaid density curves and inset statistics, as well as a normal probability plot and fit statistics for assessing normality.
Views: 4368 SAS Software