Embark on a comprehensive 6-hour journey through the world of machine learning with this in-depth tutorial. Explore the distinctions between AI, ML, DL, and Data Science before diving into core concepts like regression and classification. Master essential algorithms including Linear Regression, Ridge and Lasso Regression, Logistic Regression, Naive Bayes, KNN, Decision Trees, and ensemble methods. Gain practical implementation skills through hands-on examples. Delve into clustering techniques such as K-Means, Hierarchical, and DBSCAN. Understand crucial concepts like bias and variance. Conclude with advanced topics including XGBoost and SVM algorithms. Access supplementary materials and data science blogs for further learning.
Overview
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
Taught by
Krish Naik
