Part 1: Introduction

Introduction to the basic concepts of the subject.

• 1. What is Machine Learning?
• 2. Key Concepts.
• 3. Regression vs Classification.
• 4. Supervised vs Unsupervised Learning.
• 5. Introduction to Sklearn.

Part 2: Preprocessing.

In this section we show you how to prepare data by cleaning and transforming it, ensuring models perform accurately.

• 1. Importance of Preprocessing.
• 2. Normalization.

Part 3: Regression.

Regression is a type of supervised learning used for predicting continuous values based on input features. We'll show you some basic techniques used in ML to do regression.

• 1. Cost/Loss Function.
• 2. Linear Regression - Least Squares.
• 3. Linear Regression - Gradient Descent.
• 4. Regularization.

Part 4: Classification.

Classification is a supervised learning task used to predict categorical outcomes by assigning data points to predefined classes or labels. This section focus on explaining the concepts a some models for classification.

• 1. Loss Function.
• 2. Gradient Descent.
• 3. Performance Metrics.
• 4. Logistic Regression.
• 5. Perceptron.
• 6. SVM.
• 7. Cross-Validation.

Part 5: Trees.

Decision trees are a popular model used for both classification and regression tasks. They split the data into subsets based on feature values, creating a tree-like structure of decisions that lead to final predictions.

• 1. Basic Concepts.
• 2. Shannon Entropy and Gini Impurity.
• 3. Decision Trees.
• 4. Classification Trees.
• 5. Regression Trees.
• 6. Random Forest.

Part 6: Clustering.

Clustering is an unsupervised learning technique used to group similar data points together based on their features. The goal is to partition the data into distinct clusters where items within each cluster are more similar to each other than to those in other clusters.

• 1. Concepts.
• 2. K-Means.
• 3. Hierarchical Clustering.
• 4. Divisive Method.
• 5. Density-Based Clustering.
• 6. DBSCAN.

Part 7: Reinforcement Learning.

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards over time. Unlike supervised learning, it doesn't rely on labeled data; instead, it learns from the consequences of its actions.

• 1. Characteristics.
• 2. Framework.
• 3. Exploration and Exploitation.
• 4. Q-Learning Algorithm.
• 5. SARSA Algorithm.
• 6. Non-Deterministic Rewards and Actions.

Part 8: Dimensionality Reduction.

Dimensionality reduction is the process of reducing the number of features (or dimensions) in a dataset while retaining as much of the important information as possible.

• 1. Singular Value Decomposition (SVD).
• 2. Principal Component Analysis (PCA).