Artificial Intelligence & Machine Learning

Course Overview

Artificial Intelligence & Machine Learning Program is designed to make you job-ready with practical skills in Python, Data Science, Machine Learning, Deep Learning and Generative AI.
This course includes real-world projects, deployment training and interview preparation.

MODULE 1: Python Programming & Data Science
Python Programming

  • Python fundamentals

  • Variables & operators

  • Conditional statements & loops

  • Functions & lambda functions

  • Lists, tuples, dictionary, sets

  • List comprehensions

  • File handling

  • JSON module

  • Object Oriented Programming (OOPs)

Data Handling & Visualization

  • Data collection

  • Data preprocessing

  • Data cleaning

  • Data visualization

  • NumPy

  • Pandas

  • Matplotlib

  • Seaborn

Mini Project: Data analysis dashboard

MODULE 2: Mathematics for AI

  • Statistics fundamentals

  • Probability

  • Linear Algebra

  • Calculus basics

  • Central Limit Theorem

  • Mean, median, variance

  • Bias vs variance

MODULE 3: Machine Learning
Supervised Learning

  • Regression & classification

  • Linear regression

  • Logistic regression

  • Naive Bayes

  • KNN

  • Decision Trees

Unsupervised Learning

  • Clustering

  • K-means

  • DBSCAN

  • PCA (Dimensionality Reduction)

Reinforcement Learning

  • Agent & environment

  • Reward system basics

ML Concepts

  • Precision

  • Recall

  • F1 score

  • Overfitting & underfitting

  • Model evaluation

Tools

  • Scikit-learn

  • Kaggle workflow

Projects:

  • House price prediction

  • Customer segmentation

  • Recommendation system

MODULE 4: Deep Learning

  • Neural networks fundamentals

  • Perceptron

  • Forward propagation

  • Backward propagation

  • Feedforward Neural Network (FNN)

  • Convolutional Neural Network (CNN)

  • Recurrent Neural Network (RNN)

  • LSTM

  • Transformers

Frameworks

  • PyTorch

  • TensorFlow

  • Keras

  • PyTorch vs TensorFlow

Projects:

  • Image classification

  • Chatbot

  • Sentiment analysis

MODULE 5: Generative AI & LLMs

  • Introduction to Generative AI

  • Large Language Models (LLMs)

  • Natural Language Processing (NLP)

  • GAN (Generative Adversarial Networks)

  • RAG (Retrieval Augmented Generation)

  • Agentic AI

Tools & APIs

  • OpenAI APIs

  • GitHub Copilot

  • Cursor AI

  • Claude AI

Project: Build your own AI assistant

MODULE 6: AI Engineering Stack

  • Flask (AI web app development)

  • Frontend basics (HTML, CSS, JavaScript)

  • SQL for Data Science

  • Git & GitHub

  • Docker

  • Kubernetes basics

  • Model deployment on cloud

Project: Deploy ML model live

MAJOR PROJECTS

  • Finance prediction system

  • E-commerce recommendation engine

  • Medical prediction model

  • Resume analyzer AI

  • Chatbot with RAG

  • GenAI assistant

  • Industry domain projects

Career Opportunities

  • AI Engineer

  • Machine Learning Engineer

  • Data Scientist

  • GenAI Developer

Course Highlights

  • Live practical training

  • Real-world projects

  • Internship

  • Placement assistance

  • Resume building

  • Interview preparation

  • Industry expert sessions

Short Website Description

Master Artificial Intelligence, Machine Learning, Deep Learning and Generative AI with real-world projects and become job-ready AI Engineer in 6 months.

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