Python for ML – 4 Weeks

Course Summary

Course Duration: 4 weeks
Mode: Online / Face-to-Face (Pune)
Instructor Support: Live sessions and Q&A
Timings: 2-hour sessions, thrice a week

Machine Learning is built on Python — but to use it effectively and step into the world of Artificial Intelligence, you need to go beyond the basics.
This course equips you with the programming, data handling, and visualization skills required to step confidently into AI and ML development.

Designed for both students and professionals, this 4-week intensive bootcamp provides hands-on programming experience, mini-projects, and a capstone project that directly bridges into an AI/ML workflow.



Modules – What Will You Learn?

Module 1: Beginning Python Programming

Syntax, data types, operators

Conditionals and loops

Lists, tuples, sets, dictionaries

Functions and mini-projects (calculator, guessing game, word counter)

Module 2: Intermediate Python

Advanced functions (*args, **kwargs, lambdas)

Error handling, file I/O

Modules and packages

Object-Oriented Programming basics

Mini-projects: contact book, student/bank account class

Module 3: NumPy Foundations

Arrays vs lists, creation and attributes

Array operations, slicing, masking, broadcasting

Linear algebra operations and random number generation

Mini-project: Implement gradient descent for linear regression

Module 4: Data Visualization

Line plots, bar charts, scatter plots, histograms

Subplots, annotations, styling

Seaborn: distribution plots, scatter with regression, heatmaps

Mini-project: Titanic dataset visualization and survival analysis

Module 5: Pandas for Data Handling

Series and DataFrames

Reading/writing CSV, Excel, JSON

Filtering, sorting, handling missing values

Groupby, aggregation, joins, pivot tables

Pandas plotting and time series basics

Mini-project: Sales dataset analysis

Module 6: Advanced Concepts & Projects

List/dict comprehensions, generators, decorators

Virtual environments and package management

Git & GitHub for version control and project repositories

Capstone Projects

Tools You Will Learn

GitHub, Python, Scikit-Learn, PyTorch, NumPy, Matplotlib, Jupyter, Pandas

Who Should Enroll?

Learners who will benefit the most:

Learners bridging the gap between Python basics and applied ML

Undergraduate and postgraduate students aspiring to build a career in AI/ML

Working professionals from software or IT backgrounds

Non-technical professionals transitioning into AI/ML

Entry-level job seekers specializing in AI/ML