25 Key Machine Learning Algorithms - Math, Intuition, Python
25 Modules
350 Pages
>20h Reading Time
Beginner to Intermediate
Book Overview
Do you want to understand machine learning algorithms and how artificial intelligence works but don’t know where to start? Or perhaps you already have some knowledge and want to deepen your understanding of AI-driven algorithms? - This course is exactly what you need!
Each lesson is designed to provide clear, structured learning with three essential components:
- Theory – A deep dive into the mathematical concepts behind each algorithm
- Examples – Simple scenarios to illustrate how each algorithm works
- Implementation – Step-by-step Python coding to bring each algorithm to life
Whether you're preparing for technical interviews, building your ML foundation, or advancing your career in artificial intelligence, this course provides the structured knowledge you need to understand and apply machine learning algorithms effectively.
Book Structure
The course is organized into 25 modules, each focusing on one key algorithm:
- 1. Simple Linear Regression
- 2. Multiple Linear Regression
- 3. Logistic Regression
- 4. Decision Trees
- 5. K-means
- 6. Model Evaluation
- 7. Naive Bayes
- 8. Ridge Regression
- 9. Bagging
- 10. Random Forest
- 11. Boosting
- 12. LASSO
- 13. KNN
- 14. Gradient Boosting
- 15. PCA - Principal Component Analysis
- 16. XGBoost
- 17. LDA - Linear discriminant analysis
- 18. QDA - Quadratic discriminant analysis
- 19. Agglomerative Hierarchical Clustering
- 20. Hard-Margin SVM
- 21. SVM
- 22. DBSCAN
- 23. t-SNE
- 24. Isolation Forest
- 25. Perceptron
Key Features
Clear Mathematical Explanations
Every algorithm explained with formulas, derivations, and intuitive visual explanations.
Python Implementation
Complete Python code for each algorithm, built from scratch. Understand exactly how algorithms work.
Lifetime Access
Get lifetime access to the complete book in PDF forever. Study at your own pace, anytime, anywhere.
Scientific Yet Simple
A clear and approachable book that explains academic ML topics in plain language, supported by examples and intuitive insights.
Requirements
Basic Python Knowledge
Familiarity with Python fundamentals and basic usage of NumPy (arrays, vectors, simple operations).
Mathematical Foundations
Basic understanding of mathematics (especially linear algebra) to better grasp how machine learning models work.
Who Should Take This Course?
- Students starting their machine learning journey
- Aspiring Data Scientists and Machine Learning Engineers
- Beginners in Machine Learning who don’t know where to start
- Those looking for a balance between simple explanations and mathematical formalism
- People who prefer reading and analyzing rather than watching long lectures
