**1. Intro to AI, Machine Learning, Deep Learning**

**a. Supervised Learning
b. Unsupervised Learning
c. Regression and Classifications
d. Intro to dataset: Train, Test, Validation
e. Hyper-parameters**

**2. Linear Regression**

**a. Linear Regression Theory
b. Weights and Biases
c. Loss/Cost Function
d. Learning Rate
e. Gradients
f. Gradient Descent**

**3. Coding Linear Regression in Python**

**4. Logistic Regression**

**a. Classification recap
b. Loss Function for classification**

**5. Features and Targets in ML**

**6. Coding Logistic Regression**

**7. Understanding the Performance of your model**

**8. Exploration of various dataset and usage of Regression and Classification**

**9. Theory and Practical of SVM10. Theory and Practical of K-nearest Neighbors
11. Introduction to Neural Networks
12. N Layers Feed Forward Neural Networks
13. Activation Functions
14. Regularization
15. Hyper parameters in Feed Forward Neural Networks
16. Introduction and Practical to Convolutional Neural Networks
17. Some Practical Applications and Projects of Convolutional Neural
Networks
18. Transfer Learning and Fine Tuning
19. Basics of Object Detection, Image Classification, Neural Style Transfers
20. Introduction to Recurrent Neural Network
21. Introduction to usage of RNN in NLP
22. Generating Texts with Recurrent Neural Network
23. Sentiment Analysis with Recurrent Neural Network**

**24. Introduction to Advance Architecture in RNN**

**a. LSTM
b. GRU
c. Vanishing and Exploding Gradients**

**25. Introduction to Generative Models
26. Generative Adversarial Networks
27. Introduction to Capsule Networks
28. Introduction to Reinforcement Learning
29. Introduction to Deep Reinforcement Learning
30. Next Steps**