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**Mathematics for Machine Learning **

1. Introduction to Calculus, Linear Algebra, Probability, Statistics and Random Variables

2. Introduction to Python, numpy, pandas etc.

3. Python assignments.

**Machine Learning Basics**

1. Fundamentals of Machine Learning

2. Application in Machine Learning- Classification, Regression etc.

3. Introduction to the theory and algorithms of :

→ Supervised Learning

→ Semi Supervised Learning

→ Unsupervised Learning

→ Graphical Models

→ Predictive Modelling

**Practical Machine Learning-Frameworks**

1. Machine Learning Frameworks :

→ Google COLAB

→ Sci-kit-learn

→ TensorFlow

→ PyTorch

→ Keras

2. Industry grade tools and technologies for implementing a practical machine learning project

3. Assignments – classification, regression and mathematical models

Quiz

**Neural Network and Deep Learning **

1. Introdution to theory of neural networks and stochastic gradient descent

2. Deep neural networks, CNN, RNN, Auto Encoders

3. LSTM, GAN, Capsule networks

**Practical Machine Learning – Your own models**

1. Implementing a Neural Network from scratch

2. Implementing a Deep Neural Network (CNN, RNN, GAN) in Tensorflow/PyTorch

3. Developing AI projects and practical caveats in implementing machine learning models

4. Organizing Machine Learning Projects

**Research and Applications**

1. Applications of AI in Industry and Academia

2. Computer Vision

3. Natural Language Processing

4. What’s hot in AI research – a discussion on state of the art and recent trends in AI

Quiz