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Kalamassery, Kochi, Ernakulam
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Machine Learning
Machine Learning
Curriculum
4 Sections
20 Lessons
10 Weeks
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Module 01
Introduces Machine Learning concepts , Basic of Parameter Estimation and Supervised learning , in particular Regression
4
1.1
Introduction to ML
1.2
Basics of Parameter Estimation
1.3
Supervised Learning
1.4
Linear Regression
Module 02
Discusses Naive bayes classifier and KNN classifier. Generalization and Overfitting concepts, Regulariztion. Evaluation measures
4
2.1
Naive Bayes
2.2
KNN Classifier
2.3
Evaluation_Measures_part1
2.4
Evaluation_Measures_part2
Module 03
Neural Networks and Decision Trees
3
3.1
Neural Networks_part1
3.2
Neural Networks_part2
3.3
Decision_Tree
Module 04
Clustering , Dimensionality Reduction, Ensembling and Resampling Methods
9
4.1
K means
4.2
Divisive Clustering
4.3
Agglomerative Clustering_Part_1
4.4
Agglomerative Clustering_Part_2
4.5
DBSCAN
4.6
Ensembling_Part1
4.7
Ensembling_Part2
4.8
Bagging_and_Boosting
4.9
Resampling_Methods
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