Course Curriculum


  Section 1 : Machine Learning Basics and its Life Cycle
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  Section 2 : Machine Learning Models introduction, Tensorflow Basics , Pytorch Basics
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  Section 3: Gradient Descent Algorithm
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  Section 4 : Classification Models and Logistic Regression
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  Section 5 : Naive Bayes Classification
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  Section 6 : Decision Tree Classifier
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  Section 7 : Random Forest classifier
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  Section 8 : Support Vector Machines Classifier
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  Section 9 : Recommendation Systems
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  Section 10 : KNN algorithm
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  Section 11 : R language essentials for Machine Learning
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