Dear Gopal Krishna Sharma

You can start with this useful list of contents:

I. Machine Learning

1. Fundamental algorithms:

• Linear regression,

• Logistic regression, and

• Statistical modeling.

2. Advanced algorithms:

• Decision Tree,

• Random Forest,

• Gradient boosting, and

• K-means clustering

3. Model selection, evaluation, and interpretation concepts:

• Regularization,

• Curse of Dimensionality, and

• Cross-validation

4. Supervised and unsupervised learning tools:

• Scikit-learn,

• XGBoost

II. Deep Learning

1. Overview of Neural Networks.

2. Backpropagation techniques

3. Radial Basis functions

4. Principles of Deep Neural Networks.

5. Common Deep Neural Network configurations:

• RNNs,

• CNNs,

• MLPs,

• LSTMs

6. Generative Deep Learning and GANs

7. Engineering Frameworks:

• Keras,

•TensorFlow,

• PyTorch

III. Reinforcement Learning

1. Monte Carlo Method

2. Q-Learning Method

3. Deep Q Network Method

4. Q-Learning with Normalized Advantage Functions

Reference Materials:

1. MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence

2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

3. Deep Learning with Python by FRANÇOIS CHOLLET

Regards