Learn · Introduction to Machine Learning - IITKGP
Lecture 21 of 45Week 4
Lecture 21: Python Exercise on Naive Bayes
Prof. Sudeshna Sarkar · IIT Kharagpur
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- Introduction to Machine Learning - IITKGP, IIT Kharagpur (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Lecture 01: Introduction
- Lecture 02 : Different Types of Learning
- Lecture 03 : Hypothesis Space and Inductive Bias
- Lecture 04 : Evaluation and Cross-Validation
- Lecture 5: Tutorial - I
- Lecture 06 : Linear Regression
- Lecture 07 : Introduction to Decision Trees
- Lecture 08 : Learning Decision Tree
- Lecture 09 : Overfitting
- Lecture 10: Python Exercise on Decision Tree and Linear Regression
- Lecture 11: Tutorial - II
- Lecture 12: k-Nearest Neighbour
- Lecture 13: Feature Selection
- Lecture 14: Feature Extraction
- Lecture 15: Collaborative Filtering
- Lecture 16: Python Exercise on kNN and PCA
- Lecture 17: Tutorial III
- Lecture 18: Bayesian Learning
- Lecture 19: Naive Bayes
- Lecture 20: Bayesian Network
- Lecture 21: Python Exercise on Naive Bayes
- Lecture 22: Tutorial 4
- Lecture 23 : Logistic Regression
- Lecture 24: Introduction Support Vector Machine
- Lecture 25: SVM : The Dual Formulation
- Lecture 26: SVM : Maximum Margin with Noise
- Lecture 27: Nonlinear SVM and Kernel Function
- Lecture 28: SVM : Solution to the Dual Problem
- Lecture 29: Python Exercise on SVM
- Lecture 30 : Introduction
- Lecture 31 : Multilayer Neural Network
- Lecture 32: Neural Network and Backpropagation Algorithm
- Lecture 33: Deep Neural Network
- Lecture 34 : Python Exercise on Neural Network
- Lecture 35: Tutorial 6
- Lecture 36 : Introduction to Computational Learning Theory
- Lecture 37 : Sample Complexity : Finite Hypothesis Space
- Lecture 38: VC Dimension
- Lecture 39: Introduction to Ensembles
- Lecture 40: Bagging and Boosting
- Lecture 41: Introduction to Clustering
- Lecture 42: Kmeans Clustering
- Lecture 43: Agglomerative Hierarchical Clustering
- Lecture 44: Python Exereise on Kmeans Clustering
- Tutorial 8