1. Introduction to DS
2. Installing python
3. Python Basics
4. Python libraries
5. Type of data
6. Mean mode and median
7. Variation and standard deviation
8. Probability
9. Conditional probability
10. Bayes theory
11. Linear regression
12. Polinomial regression
13. K-Means
14. Logistic regression
15. Poision regression
16. Decision tree
17. Random forest
18. Chi square test
19.Supervised Vs.Unsupervised learning
20. K-Means clustering
21. KNN- Concept
22. Confusion Matrix
23. K-Fold cross validation
24. Handling unbalanced data
25. TF/IDF
26. A/B Testing
27. Deep Learning introduction
28. Tensorflow and Keras library
29. RNN Network in deep learning
30. CNN Network in deep learning
31.Hyperparameter tuning