25 Key Machine Learning Algorithms - Math, Intuition, Python

Learn the core ML algorithms with clear math, intuitive explanations, and Python implementation.

What you'll learn

Master 25 most important ML algorithms from scratch

Step-by-step examples with math calculations

Implement each algorithm FROM SCRATCH!

Master the essential theory – no interview will be a problem

Mathematics behind ML algorithms

Intuition behind mathematical formulas

Regression, Classification, Clustering, Dimensionality Reduction, and Anomaly Detection

Ready to build your own ML projects

Enhance your programming skills in Python

Requirements

Course from the basics (for beginners)

Basic mathematical knowledge

Basic knowledge of Python (numpy)

Description

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!In this course, you’ll master 25 key machine learning algorithms:Simple Linear RegressionMultiple Linear RegressionLogistic RegressionDecision TreesK-meansModel EvaluationNaive BayesRidge RegressionBaggingRandom ForestBoostingLASSOKNNGradient BoostingPCA - Principal Component AnalysisXGBoostLDA - Linear discriminant analysisQDA - Quadratic discriminant analysisAgglomerative Hierarchical ClusteringHard-Margin SVMSVMDBSCANt-SNEIsolation ForestPerceptronEach lesson is designed to provide clear, structured learning with three essential components:Theory – A deep dive into the mathematical concepts behind each algorithmExamples – Simple scenarios to illustrate how each algorithm worksImplementation – Step-by-step Python coding to bring each algorithm to lifeWhy This Course Stands Out:No long videos – Just focused learning! This course is perfect for those who prefer reading over passive video watching.Math made simple – Algorithms are explained in an accessible way, with intuitive examples to help you understand their logic.Hands-on coding – You’ll implement every algorithm from scratch, ensuring you truly understand the process.Ready to start your journey in Machine Learning?

Overview

Section 1: Getting Started with Google Colab

Lecture 1 How to start?

Section 2: 1. Simple Linear Regression

Lecture 2 Intro

Lecture 3 Simple Linear Regression

Section 3: 2. Multiple Linear Regression

Lecture 4 Intro

Lecture 5 Multiple Linear Regression

Section 4: 3. Logistic Regression

Lecture 6 Intro

Lecture 7 Logistic Regression

Section 5: 4. Decision Trees

Lecture 8 Intro

Lecture 9 Decision Trees

Section 6: 5. K-means

Lecture 10 Intro

Lecture 11 K-means

Section 7: 6. Model Evaluation

Lecture 12 Intro

Lecture 13 Model Evaluation

Section 8: 7. Naive Bayes

Lecture 14 Intro

Lecture 15 Naive Bayes

Section 9: 8. Ridge Regression

Lecture 16 Intro

Lecture 17 Ridge Regression

Section 10: 9. Bagging

Lecture 18 Intro

Lecture 19 Bagging

Section 11: 10. Random Forest

Lecture 20 Intro

Lecture 21 Random Forest

Section 12: 11. Boosting

Lecture 22 Intro

Lecture 23 Boosting

Section 13: 12. LASSO

Lecture 24 Intro

Lecture 25 LASSO

Section 14: 13. KNN - K Nearest Neighbors

Lecture 26 Intro

Lecture 27 KNN - K Nearest Neighbors

Section 15: 14. Gradient Boosting

Lecture 28 Intro

Lecture 29 Gradient Boosting

Section 16: 15. PCA - Principal Component Analysis

Lecture 30 Intro

Lecture 31 PCA - Principal Component Analysis

Section 17: 16. XGBoost

Lecture 32 Intro

Lecture 33 XGBoost

Section 18: 17. LDA - Linear Discriminant Analysis

Lecture 34 Intro

Lecture 35 LDA - Linear Discriminant Analysis

Section 19: 18. QDA - Quadratic Discriminant Analysis

Lecture 36 Intro

Lecture 37 QDA - Quadratic Discriminant Analysis

Section 20: 19. Agglomerative Hierarchical Clustering

Lecture 38 Intro

Lecture 39 Agglomerative Hierarchical Clustering

Section 21: 20. Hard-Margin SVM

Lecture 40 Intro

Lecture 41 Hard-Margin SVM

Section 22: 21. SVM - Support Vector Machine

Lecture 42 Intro

Lecture 43 SVM - Support Vector Machine

Section 23: 22. DBSCAN

Lecture 44 Intro

Lecture 45 DBSCAN

Section 24: 23. t-SNE

Lecture 46 Intro

Lecture 47 t-SNE

Section 25: 24. Isolation Forest

Lecture 48 Intro

Lecture 49 Isolation Forest

Section 26: 25. Perceptron

Lecture 50 Intro

Lecture 51 Perceptron

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