| 1 |
lecture 1 - Introduction to Machine Learning
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2022-06-21
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| 2 |
lecture 2 - Linear Regression, Decision Trees, Overfitting
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2022-06-21
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| 3 |
lecture 3 - Instance Based Learning, Feature Reduction, Collaborative Filtering,
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2022-06-21
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| 4 |
lecture 4 - Logistic Regression, Support Vector Machine
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2022-06-21
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| 5 |
lecture 5 - Clustering Techniques & Gaussian Mixture Model
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2022-06-21
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| 6 |
lecture 6 - Ensemble Models
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2022-06-21
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| 7 |
lecture 7 - Reinforcement Learning
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2022-06-21
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| 8 |
lecture 8 - Multiclass Classification
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2022-06-21
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| 9 |
lecture 9 - Probability & Bayes Learning
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2022-06-21
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| 10 |
lecture 10 - PCA & Autoencoders
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2022-06-21
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| 11 |
lecture 11 - Experimental Evaluation of Learning Algorithms
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2022-06-21
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| 12 |
lecture 12 - Implementation of Machine Learning Algorithms in Python
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2022-06-21
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