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Introduction
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Classroom Contents
Complete Machine Learning in 6 Hours - Krish Naik
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- 1 Introduction
- 2 AI Vs ML vs DL vs Data Science
- 3 Machine LEarning and Deep Learning
- 4 Regression And Classification
- 5 Linear Regression Algorithm
- 6 Ridge And Lasso Regression Algorithms
- 7 Logistic Regression Algorithm
- 8 Linear Regression Practical Implementation
- 9 Ridge And Lasso Regression Practical Implementation
- 10 Naive Baye's Algorithms
- 11 KNN Algorithm Intuition
- 12 Decision Tree Classification Algorithms
- 13 Decision Tree Regression Algorithms
- 14 Practical Implementation Of Deicsion Tree Classifier
- 15 Ensemble Bagging And Bossting Techniques
- 16 Random Forest Classifier And Regressor
- 17 Boosting, Adaboost Machine Learning Algorithms
- 18 K Means Clustering Algorithm
- 19 Hierarichal Clustering Algorithms
- 20 Silhoutte Clustering- Validating Clusters
- 21 Dbscan Clustering Algorithms
- 22 Clustering Practical Examples
- 23 Bias And Variance Algorithms
- 24 Xgboost Classifier Algorithms
- 25 Xgboost Regressor Algorithms
- 26 SVM Algorithm Machine LEarning Algorithm
