* Introduction to Python Programming
* key Python Libraries(NumPy, SciPy,Pandas,Colab)
* Refresher for Math Concepts
* Overview of AI and ML Concepts
* Overview of Python and its libraries for ML (Pandas,Matplotlib etc)
* Demystifying ML
* ML Avatars
* Simple but elegant Algorithms
* Ideas about Practical issues such as Overfitting Etc.
* Formulating Real World Problems for AI/ML
* Classification and Regression Problems
* Intuitive and simple Algorithms
* Representation of the World and Real Data
* Linear Algorithms,Optimization and Training
* End-to-end Problem Solving
* Non-linear Solution and MLP
* Gradient Descent and Back-propagation
* Decision Trees,Random Forests and Ensembles
* Visualization,Data Prep , Unsupervised Learning
* Principles and Practice of ML
* Support Vector Machines and Kernels
* Introduction to DL
* Convolutional Neural Networks
* Auto-Encoders
* Recurrent Neural Networks
* Overview of Advanced topics in dl
* Human in the loop solutions,deployment
* Introduction to DL
* Non-linear Solution and MLP