Fundamentals of Machine Learning

Contents

1. Machine Learning Demystified

1. Introduction

2. Basics of Data Science

3. Machine Learning: A Subset of Artificial Intelligence

4. Getting Started with Machine Learning: Key Concepts and Terminology

4.1 Data

4.2 Traditional Computing and Machine Learning

4.3 Define Machine Learning

4.4 Train-Test Modeling

4.5 Agent in Machine Learning

5. Significance of Artificial Intelligence and Machine Learning

6. Types of Machine Learning

6.1 Supervised Learning

6.2 Unsupervised Learning

6.3 Reinforcement Learning

6.4 Semi-Supervised Learning

6.5 Self-Supervised Learning

6.6 Ensemble Learning  

7. Dimensionality Reduction

8.Machine Learning Life Cycle

2. Data Preprocessing

1. Introduction

1.1 Benefits of Data Preprocessing

2. Steps in Data Preprocessing

2.1 Data Collection

2.2 Data Cleaning

2.3 Data Transformation

2.4 Data Reduction

2.5 Data Discretization

3. Data Preprocessing for Natural Language Processing (NLP)

4. Data Preprocessing for Computer Vision

3. Basics of Regression: Linear Regression

1. Introduction

2. Regression

3. Terminologies Related to Regression Analysis in Machine Learning

4. Linear Regression   

4.1 Simple Linear Regression

4.2 Multiple Linear Regression

5. Residual Analysis   

6. Covariance

4. Advanced Topics in Regression Analysis

1. Introduction

2. Multiple Variables in Regression

3. Non-Normality and Heteroscedasticity

4. Hypothesis Testing in Regression Models

5. Confidence Intervals of Slope in Regression Analysis

6. R-square and Goodness of Fit in Regression Analysis

7. Influential Observations and Leverage in Regression Analysis

8. Regularization Methods in Regression Analysis

9. Categorical Variables in Regression

10. Support Vector Regression and Decision Tree Regression

5. Linear Models for Classification

1. Introduction

2. Logistic Regression

2.1 Comparison with Linear Regression

2.2 Components of Logistic Regression

2.3 Multiclass Logistic Regression Using Logistic Regression

2.4 Threshold in Logistic Regression

2.5 Advantages of Logistic Regression

2.6 Limitations of Logistic Regression

3. Support Vector Machine

3.1 Working of SVM

3.2 Linear Modelling Using SVM

3.3 Grid Search

6. Non-Linear Models for Classification

1. Introduction

2. Non-Linear Model Using Support Vector Machines for Classification

2.1 Polynomial Kernel  

2.2  Sigmoid Kernel

3. K-Nearest Neighbors (KNN)

3.1 Working of KNN

3.2 Computational Geometry in KNN: Voronoi Diagrams and Delaunay   
    Triangulations

3.3 Wilson Editing and its Role in Improving KNN Efficiency

4. Naïve Bayes

4.1 Probability Estimation Techniques

4.2 M-Estimates in Naïve Bayes

4.3 Data Preprocessing Requirements for Effective Modeling

5. Naïve Bayes vs. K-Nearest Neighbors (KNN)

7. Model Evaluation

1. Introduction

2. Regression Metrics

3. Classification Metrics

8. Tree Models

1. Introduction

2. Decision Trees

2.1 Decision Trees Vs. Other Algorithms

2.2 Tree Concepts

2.3 Decision Tree Splitting Criteria

2.4 Decision Tree Algorithms

2.5 Working of Decision Trees

3. Random Forest

3.1 Introduction to Random Forest

3.2 Working of Random Forest

3.3 Feature Importance Using Random Forest

9. Dimensionality Reduction: PCA and LDA

1. Introduction

2. Principal Component Analysis

2.1 Mathematical foundation of Principal Component Analysis

2.2 Selecting the Number of Principal Components

2.3 Implementation of PCA

3. Linear Discriminant Analysis

3.1 PCA Vs. LDA  

3.2 Mathematical Foundations of Linear Discriminant Analysis (LDA)

3.3 Steps in Linear Discriminant Analysis (LDA)

3.4 Implementation of LDA

4. PCA and LDA – Role in Hyperparameter Tunning

10. Unsupervised Learning Models

1. Introduction

2. Clustering

2.1 Distance Measures in Clustering

2.2 Different Clustering Methods

3. Association Rule Mining

3.1 Algorithms for Association Rule Mining

4. Recommendation Engine

5. Challenges and Best Practices in Unsupervised Learning

Appendix – A

Appendix – B

Appendix – C

Appendix – D