ML Book 1 - Machine Learning Foundations for Absolute Beginners: Concepts and Techniques

1. Introduction

1.1 What is Machine Learning?

1.2 Why Machine Learning?

1.3 Types of Machine Learning

1.4 Applications of Machine Learning

1.5 Challenges and Limitations of Machine Learning

2. Data and Preprocessing

2.1 Data Sources and Formats

2.2 Data Exploration and Visualization

2.3 Data Cleaning and Transformation

2.4 Data Splitting and Sampling

2.5 Feature Engineering and Selection

3. Supervised Learning

3.1 Regression

3.2 Classification

3.3 Evaluation Metrics

3.4 Linear Models

3.5 Decision Trees and Random Forests

3.6 Support Vector Machines

3.7 K-Nearest Neighbors

3.8 Neural Networks

4. Unsupervised Learning

4.1 Clustering

4.2 Dimensionality Reduction

4.3 Association Rules

4.4 Anomaly Detection

4.5 Recommender Systems

5. Reinforcement Learning

5.1 Basic Concepts and Terminology

5.2 Markov Decision Processes

5.3 Value Functions and Bellman Equations

5.4 Policy Iteration and Value Iteration

5.5 Q-Learning and SARSA

5.6 Deep Reinforcement Learning

6. Machine Learning in Practice

6.1 Machine Learning Workflow

6.2 Machine Learning Tools and Libraries

6.3 Model Selection and Hyperparameter Tuning

6.4 Model Deployment and Monitoring

6.5 Machine Learning Ethics and Fairness