Learn to implement fundamental machine learning algorithms from scratch using Python in this comprehensive tutorial series covering K-Nearest Neighbor, Logistic Regression, Linear Regression with both Gradient Descent and Normal Equation methods, K-Means Clustering, Naive Bayes, Support Vector Machine, and Neural Networks, providing hands-on experience building these essential algorithms without relying on pre-built libraries to develop a deep understanding of their underlying mathematical principles and implementation details.

Syllabus

K-Nearest Neighbor from scratch - Machine Learning Python
Logistic Regression from Scratch - Machine Learning Python
Linear Regression Gradient Descent From Scratch in Python
Linear Regression Normal Equation Python
K-Means Clustering from Scratch - Machine Learning Python
Naive Bayes from Scratch - Machine Learning Python
SVM from Scratch - Machine Learning Python (Support Vector Machine)
Neural Network from Scratch - Machine Learning Python