Fundamentals of Statistical Modeling and Machine Learning Techniques [Video]

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Fundamentals of Statistical Modeling and Machine Learning Techniques [Video]

Pratap Dangeti

1 customer reviews
Understand various concepts related to Statistics and Machine Learning

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Video Details

ISBN 139781788833981
Course Length2 hours

Video Description

Complex 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.

Style and Approach

This course contains problem solution approach. Each video focuses on a particular task at hand, and is explained in a very simple, easy to understand manner.

Table of Contents

Statistical Terminology and Machine Learning
The Course Overview
Machine Learning
Statistical Terminology for Model Building and Validation
Bias Versus Variance Trade-Off
Machine Learning Terminology for Model Building and Validation
Linear Regression Versus Gradient Descent
Machine Learning Losses
Train, Validation, and Test Data
Cross-Validation and Grid Search
Machine Learning Model Overview
Linear Regression
Compensating Factors in Machine Learning Models
Simple Linear Regression from First Principles
Simple Linear Regression Using Wine Quality Data
Multi-Linear Regression
Linear Regression Model – Ridge Regression
Linear Regression Model – Lasso Regression
Logistic Regression Versus Random Forest
Maximum Likelihood Estimation
Logistic Regression
Random Forest
Variable Importance Plot

What You Will Learn

  • Introduces statistical terminology and machine learning 
  • Provides an overview of machine learning terminology for model building and validation
  • Offers practical solutions for simple linear regression and multi-linear regression
  • Compares logistic regression and random forest using examples

Authors

Table of Contents

Statistical Terminology and Machine Learning
The Course Overview
Machine Learning
Statistical Terminology for Model Building and Validation
Bias Versus Variance Trade-Off
Machine Learning Terminology for Model Building and Validation
Linear Regression Versus Gradient Descent
Machine Learning Losses
Train, Validation, and Test Data
Cross-Validation and Grid Search
Machine Learning Model Overview
Linear Regression
Compensating Factors in Machine Learning Models
Simple Linear Regression from First Principles
Simple Linear Regression Using Wine Quality Data
Multi-Linear Regression
Linear Regression Model – Ridge Regression
Linear Regression Model – Lasso Regression
Logistic Regression Versus Random Forest
Maximum Likelihood Estimation
Logistic Regression
Random Forest
Variable Importance Plot

Video Details

ISBN 139781788833981
Course Length2 hours
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