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Course Curriculum

Introduction
ML-Introduction 00:23:00
Basic Statistics
ML-Basic Statistics-1 00:44:00
ML-Basic Statistics-2 00:43:00
Data Cleaning
ML-Data Cleaning-1 00:38:00
ML-Data Cleaning-2 00:38:00
Regression Analysis
ML-Regression Analysis-1 01:03:00
ML-Regression Analysis-2 01:00:00
Logistic Regression
ML-Logistic Regression 01:00:00
Decision Trees
ML-Decision Tree-1 00:54:00
ML-Decision Tree-2 00:44:00
ML-Decision Tree-3 01:01:00
Model Selection And Cross Validation
ML-Model Selection 00:47:00
ML-Cross Validation 00:42:00
Ensemble Models
ML-Ensemble Model – Random Forest 01:07:00
ML-Ensemble Model – Boosting 00:43:00
Feature Engineering
ML-Feature Engineering 00:52:00
Natural Language Processing
ML-NLP and Text Mining 01:07:00
ML-Sentiment Anlaysis 00:31:00
Testing of Hypothesis
ML-Testing of Hypothesis 00:54:00
End to End ML Project
ML-End to End Project-1 00:29:00
ML-End to End Project-2 00:30:00
ML-End to End Project-3 00:29:00
ML-End to End Project-4 00:24:00
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