Machine Learning from scratch in depth (With One to One Live Doubt Sessions)

This comprehensive Machine Learning course is designed to provide a deep understanding of both the mathematical foundations and practical implementation of Classical Machine Learning algorithms. The course covers all major Supervised and Unsupervised Learning techniques with detailed explanations, intuitive understanding, and complete implementations from scratch using Python.

Learners will begin with the essential mathematical concepts required for Machine Learning, including Linear Algebra, Calculus, Probability, Statistics, Optimization, and Gradient Descent. The course then moves into the complete Machine Learning workflow, including data preprocessing, feature engineering, model training, evaluation, and performance optimization.

The Supervised Learning section covers algorithms such as:

1. Linear and Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Support Vector Machines (SVM)
4. Naive Bayes
5. Decision Trees
6. Random Forest
7. Ensemble Learning Methods

The Unsupervised Learning section includes:

1. K-Means Clustering
2. Hierarchical Clustering
3. DBSCAN
4. PCA and Dimensionality Reduction Techniques
5. Association Rule Learning

A major highlight of the course is the implementation of algorithms completely from scratch, helping learners understand the internal mathematics, optimization procedures, loss functions, and working principles behind each model.

The course also includes detailed coverage of performance metrics, model evaluation techniques, and practical problem-solving using real-world datasets.

This course is ideal for students, researchers, aspiring AI/ML engineers, and anyone who wants to master Machine Learning from fundamentals to advanced implementation with strong mathematical intuition.