**Fundamentals of Statistical Modeling and Machine Learning Techniques**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2 Hours | 386 MB

eLearning | Skill level: All Levels

Learn the Basic Building Blocks of Statistics and Machine Learning

Understand various concepts related to Statistics and Machine Learning

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.

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

**+ Table of Contents**

01 The Course Overview

02 Machine Learning

03 Statistical Terminology for Model Building and Validation

04 Bias Versus Variance Trade-Off

05 Linear Regression Versus Gradient Descent

06 Machine Learning Losses

07 Train, Validation, and Test Data

08 Cross-Validation and Grid Search

09 Machine Learning Model Overview

10 Compensating Factors in Machine Learning Models

11 Simple Linear Regression from First Principles

12 Simple Linear Regression Using Wine Quality Data

13 Multi-Linear Regression

14 Linear Regression Model – Ridge Regression

15 Linear Regression Model – Lasso Regression

16 Maximum Likelihood Estimation

17 Logistic Regression

18 Random Forest

19 Variable Importance Plot

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