Machine Learning

Algorithms, models, and real-world applications

41 chapters across 9 modules

1

Foundations Of Machine Learning

5 chapters

1

What is Machine Learning?

6 min read

2

The Three Paradigms

8 min read

3

The Machine Learning Workflow

7 min read

4

Bias-Variance Tradeoff, Overfitting & Underfitting

8 min read

5

Train/Test Splits & Cross-Validation Basics

9 min read

2

Data Preparation For ML

5 chapters

1

From Raw Data to Model-Ready Data

5 min read

2

Encoding Categorical Variables

8 min read

3

Feature Scaling & Normalization

7 min read

4

Feature Engineering & Selection for ML

8 min read

5

Handling Imbalanced Datasets

8 min read

3

Supervised Learning Regression

4 chapters

1

Simple & Multiple Linear Regression

9 min read

2

Regularization: Ridge, Lasso & Elastic Net

7 min read

3

Polynomial & Non-Linear Regression

6 min read

4

Evaluating Regression Models

8 min read

4

Supervised Learning Classification

6 chapters

1

Logistic Regression

8 min read

2

K-Nearest Neighbors

8 min read

3

Decision Trees

9 min read

4

Support Vector Machines

8 min read

5

Naive Bayes for Classification

7 min read

6

Evaluating Classification Models

9 min read

5

Ensemble Learning

4 chapters

1

Bagging & Random Forests

8 min read

2

Boosting: AdaBoost & Gradient Boosting

8 min read

3

XGBoost, LightGBM & Modern Boosting

7 min read

4

Stacking & Blending

8 min read

6

Unsupervised Learning

4 chapters

1

K-Means Clustering

7 min read

2

Hierarchical Clustering & DBSCAN

8 min read

3

Principal Component Analysis (PCA)

7 min read

4

t-SNE, UMAP & Association Rules

8 min read

7

Model Selection And Hyperparameter Tuning

4 chapters

1

Cross-Validation Strategies

7 min read

2

Grid Search & Random Search

7 min read

3

Bayesian Optimization & Advanced Tuning

7 min read

4

Model Selection Best Practices

8 min read

8

Reinforcement Learning For ML Practitioners

4 chapters

1

RL Recap & Where It Fits in ML

5 min read

2

Policy Gradient Methods

7 min read

3

Actor-Critic Methods

7 min read

4

From Tabular RL to Deep RL

7 min read

9

ML In Production And Capstone

5 chapters

1

Building ML Pipelines

6 min read

2

Model Deployment Basics

7 min read

3

Model Monitoring, Drift & Retraining

8 min read

4

Capstone Project: End-to-End ML System

6 min read

5

Where to Go Next

6 min read