Complete Machine Learning in 6 Hours - Krish Naik

This course surveys machine learning fundamentals and major algorithms for regression, classification, clustering, ensembles, and support vector machines. It combines mathematical and geometric intuition with practical implementations and examples, with an emphasis on explaining algorithms clearly for data science interviews.

Syllabus

Introduction
AI Vs ML vs DL vs Data Science
Machine LEarning and Deep Learning
Regression And Classification
Linear Regression Algorithm
Ridge And Lasso Regression Algorithms
Logistic Regression Algorithm
Linear Regression Practical Implementation
Ridge And Lasso Regression Practical Implementation
Naive Baye's Algorithms
KNN Algorithm Intuition
Decision Tree Classification Algorithms
Decision Tree Regression Algorithms
Practical Implementation Of Deicsion Tree Classifier
Ensemble Bagging And Bossting Techniques
Random Forest Classifier And Regressor
Boosting, Adaboost Machine Learning Algorithms
K Means Clustering Algorithm
Hierarichal Clustering Algorithms
Silhoutte Clustering- Validating Clusters
Dbscan Clustering Algorithms
Clustering Practical Examples
Bias And Variance Algorithms
Xgboost Classifier Algorithms
Xgboost Regressor Algorithms
SVM Algorithm Machine LEarning Algorithm