Requirements

Outcomes

Description

1. Introduction to Python: Environment setup, Basic I/O.

2. Data Structures

3. File Handling & Exception handling

4. Data Structures for Machine Learning - Numpy, Pandas

5. Descriptive Statistics

6. Hypothesis Testing

7. Data Visualisation 

8. Supervised Algorithm - Regression

9. Supervised Algorithm - 2 Class Classification

10. Supervised Algorithm - Multi-Class Classification

11. Supervised Algorithm - Tree Algorithms

12. Unsupervised Algorithm - Clustering

13. Unsupervised Algorithm

14. DBSCAN - Clustering Algorithm

15. Dimensionality Reduction

16. Time Series Analysis - Part I

17. Time Series Analysis - Part II

18. Text Processing - Handling different kinds of text data

19. Sentiment Analysis using text data

20. Introduction to Neural Networks

21. Creating ANN neural network for regression and classification

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