by Pratap Dangeti | Released October 2017 | ISBN: 9781788833981

Understand various concepts related to Statistics and Machine LearningAbout This VideoUnderstand the Statistical fundamentals and terminology for model building and validationHandle simple linear regression using wine quality dataExecute Ridge/Lasso regression modelPerform grid search on Random ForestImplement Logistic Regression using credit dataIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This video will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, NumPy, random Forest and so on. By the end of the course, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Show and hide more Publisher resources Download Example Code

1. Chapter 1 : Statistical Terminology and Machine Learning
   • The Course Overview 00:02:22
   • Machine Learning 00:05:28
   • Statistical Terminology for Model Building and Validation 00:19:47
   • Bias Versus Variance Trade-Off 00:03:59
2. Chapter 2 : Machine Learning Terminology for Model Building and Validation
   • Linear Regression Versus Gradient Descent 00:04:47
   • Machine Learning Losses 00:01:52
   • Train, Validation, and Test Data 00:02:07
   • Cross-Validation and Grid Search 00:04:02
   • Machine Learning Model Overview 00:05:31
3. Chapter 3 : Linear Regression
   • Compensating Factors in Machine Learning Models 00:07:24
   • Simple Linear Regression from First Principles 00:03:50
   • Simple Linear Regression Using Wine Quality Data 00:02:59
   • Multi-Linear Regression 00:07:29
   • Linear Regression Model – Ridge Regression 00:03:59
   • Linear Regression Model – Lasso Regression 00:03:07
4. Chapter 4 : Logistic Regression Versus Random Forest
   • Maximum Likelihood Estimation 00:03:38
   • Logistic Regression 00:25:14
   • Random Forest 00:08:11
   • Variable Importance Plot 00:04:38
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