Python + AI Foundations for College Students
Python + AI Foundations – Weekday Evening Batch | Oct 2026
Live online batch exclusively for college students. Classes will be conducted Monday to Friday from 7:30 PM to 8:30 PM IST for 12 weeks. The program covers Python programming, data analysis, machine learning, Generative AI, hands-on assignments, mini-projects, and a final AI project.
About This Course
Python + AI Foundations is a practical, beginner-friendly program designed specifically for college students who want to build a strong foundation in programming, data science, machine learning, and modern AI.
The course starts with Python fundamentals and gradually moves into data analysis, machine learning, and Generative AI concepts. Students will work on assignments, coding exercises, and practical projects throughout the program.
No prior Python or AI experience is required.
What You'll Learn
- Python programming fundamentals
- Variables, data types, operators, and control flow
- Functions and reusable code
- Lists, tuples, sets, and dictionaries
- File handling and exception handling
- Object-Oriented Programming in Python
- NumPy for numerical computing
- Pandas for data analysis
- Data cleaning and preprocessing
- Data visualization using Matplotlib
- Fundamentals of statistics for AI and ML
- Introduction to Artificial Intelligence and Machine Learning
- Supervised and unsupervised learning
- Linear Regression
- Logistic Regression
- Decision Trees
- K-Nearest Neighbours
- Clustering
- Training and evaluating machine learning models
- Introduction to Generative AI and Large Language Models
- Prompt engineering fundamentals
- Working with AI APIs
- Introduction to embeddings and RAG
- Building a simple AI-powered application
Hands-On Learning
Students will work on:
- Python coding exercises
- Weekly assignments
- Data analysis exercises
- Machine learning experiments
- Mini projects
- A final AI project
- GitHub-based project submissions
Projects
Data Analysis Project
Analyze a real-world dataset using Python, Pandas, and visualization libraries.
Machine Learning Project
Build and evaluate a machine learning model for a practical prediction or classification problem.
Final AI Project
Build a Python-based AI application such as a chatbot or document question-answering application.
Who Should Join?
This course is suitable for:
- Engineering students
- BCA students
- BSc Computer Science / IT students
- MCA students
- Students interested in AI, Machine Learning, Data Science, or Software Development
- Beginners who want to start programming with Python
Prerequisites
- Basic computer knowledge
- Logical thinking and willingness to practice
- No previous Python or AI experience required
Learning Outcome
By the end of the course, students will be able to write Python programs, analyze datasets, build basic machine learning models, understand modern AI concepts, and develop a working AI-powered application.
Students will also complete projects that can be showcased through GitHub as part of their learning portfolio.
Curriculum · 12 modules, 24 lessons
Module 1 — Week 1 - Python Foundations 2 lessons
- Course orientation, Python setup, IDE and first program · 120
- Variables, data types, operators, input and output · 120
Module 2 — Week 2 - Control Flow & Collections 2 lessons
- Conditions, loops and problem solving · 120
- Lists, tuples, sets, dictionaries and strings · 120
Module 3 — Week 3 - Functions & Practical Python 2 lessons
- Functions, parameters, return values and scope · 120
- Modules, exceptions, files, CSV and JSON · 120
Module 4 — Week 4 - Object-Oriented Python 2 lessons
- Classes, objects, constructors and encapsulation · 120
- Inheritance, polymorphism, packages, pip and virtual environments · 120
Module 5 — Week 5 - NumPy & Pandas 2 lessons
- NumPy arrays and numerical computing · 120
- Pandas Series and DataFrames · 120
Module 6 — Week 6 - Data Analysis & Visualization 2 lessons
- Data cleaning and preprocessing with Pandas · 120
- Data visualization with Matplotlib and exploratory data analysis · 120
Module 7 — Week 7 - AI & Machine Learning Foundations 2 lessons
- AI, ML and data science fundamentals · 120
- ML workflow: preprocessing, train/test split and model evaluation · 120
Module 8 — Week 8 - Regression & Classification 2 lessons
- Linear regression and regression metrics · 120
- Logistic regression and classification fundamentals · 120
Module 9 — Week 9 - Classification Algorithms 2 lessons
- K-Nearest Neighbours and Decision Trees · 120
- Classification evaluation: confusion matrix, precision, recall and F1-score · 120
Module 10 — Week 10 - Unsupervised ML & ML Project 2 lessons
- Clustering with K-Means · 120
- End-to-end machine learning mini project · 120
Module 11 — Week 11 - Generative AI & LLMs 2 lessons
- Generative AI, LLM fundamentals and prompt engineering · 120
- Using an LLM API from Python and building a simple AI application · 120
Module 12 — Week 12 - Embeddings, RAG & Final Project 2 lessons
- Embeddings, vector search and introduction to RAG · 120
- Final AI project build, demo, review and course assessment · 120
Cancellation & refund policy
- 7 or more days before the batch starts: 100% refund.
- Less than 7 days before the batch starts: 50% refund.
- After the batch starts: No refund will be provided after completion of the first 2 sessions.
- If a student is unable to continue due to a genuine reason, the enrollment may be transferred to the next available batch, subject to approval.
- Missed classes or non-attendance are not eligible for a refund.
- Payment gateway or transaction charges, if applicable, are non-refundable.
- If the batch is cancelled by ConfidentHire Academy, students may choose either:
- A 100% refund, or
- Transfer to another available batch.
- Approved refunds will be processed to the original payment method within 7–10 working days.
Ask a question / request a callback
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