Will be covering these topics in this course:

1. Python Basics like lists, tuples, sets, dictionaries, functions, loops, control flows, decorators, generators, namedtuple, strings, slicing, debugging, jupyter notebook, unit testing using pytest, multithreading, OOPs concepts, hands-on with their implementation, sockets, lambdas, exceptions, re module, defaultdict, working with datetime, file handling, csv files, installing packages uing pip, with some small guided projects.

2. Data Analysis in Python:

Cleaning and preparing data in Python, working with numpy and pandas extensively with each and every minute details, working on real-world datasets and doing hands-on experiments, data structures used in numpy and pandas, merging dataframes, pivot tables, Data visualization using matplotlib, etc.

3. Introduction to Machine Learning :

Building our first neural network and learn some of the basic concepts behind ML, creating and training our neural network that can recognize images, etc..., using keras, tensorflow and scikit-learn during the process, compiling and training the model and using it to pedict values with real hands-on examples.