Course Details

Become a job-ready AI & Machine Learning professional through a practical, project-based learning experience. This course takes you from Python and data fundamentals to machine learning, deep learning, and modern Generative AI. Learn how to clean and analyze data, build and evaluate machine learning models, work with neural networks, and develop intelligent applications using real-world datasets. You will also explore Natural Language Processing, computer vision, and Generative AI concepts. By the end of the course, you will have hands-on experience building AI solutions and projects that can strengthen your portfolio and prepare you for AI/ML internships and entry-level roles.

Module 1: Introduction to AI & Machine Learning
• Introduction to Artificial Intelligence
• Introduction to Machine Learning
• AI vs Machine Learning vs Deep Learning
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• AI Applications in the Real World
• Setting Up the AI/ML Development Environment

Module 2: Python for AI & ML
• Python Fundamentals
• Variables and Data Types
• Conditional Statements
• Loops
• Functions
• Lists, Tuples and Dictionaries
• Object-Oriented Programming Basics
• Exception Handling
• File Handling
• Working with Jupyter Notebook

Module 3: Data Analysis & Preprocessing
• Introduction to NumPy
• NumPy Arrays and Operations
• Introduction to Pandas
• DataFrames and Series
• Data Import and Export
• Data Cleaning
• Handling Missing Values
• Removing Duplicates
• Data Transformation
• Feature Selection
• Exploratory Data Analysis

Module 4: Mathematics & Statistics for ML
• Linear Algebra Basics
• Vectors and Matrices
• Probability Fundamentals
• Mean, Median and Mode
• Variance and Standard Deviation
• Probability Distributions
• Correlation
• Covariance
• Statistical Sampling
• Hypothesis Testing Basics

Module 5: Data Visualization
• Introduction to Data Visualization
• Matplotlib
• Seaborn
• Line Charts
• Bar Charts
• Histograms
• Scatter Plots
• Box Plots
• Correlation Heatmaps
• Visualizing Data Insights

Module 6: Machine Learning Fundamentals
• Machine Learning Workflow
• Training and Testing Data
• Feature Engineering
• Model Selection
• Model Training
• Model Evaluation
• Overfitting and Underfitting
• Bias and Variance
• Cross-Validation

Module 7: Supervised Learning
• Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• K-Nearest Neighbors
• Support Vector Machines
• Naive Bayes
• Classification and Regression Problems
• Model Performance Evaluation

Module 8: Unsupervised Learning
• Introduction to Unsupervised Learning
• Clustering
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis
• Dimensionality Reduction
• Anomaly Detection
• Customer Segmentation

Module 9: Advanced Machine Learning
• Feature Engineering
• Feature Scaling
• Hyperparameter Tuning
• Grid Search
• Random Search
• Ensemble Learning
• Gradient Boosting
• XGBoost Fundamentals
• Model Optimization
• ML Pipelines

Module 10: Deep Learning
• Introduction to Deep Learning
• Neural Networks
• Neurons and Activation Functions
• Forward and Backpropagation
• Loss Functions
• Optimizers
• Building Neural Networks
• Introduction to TensorFlow
• Introduction to Keras
• Model Training and Evaluation

Module 11: NLP & Computer Vision
• Introduction to Natural Language Processing
• Text Preprocessing
• Tokenization
• Text Classification
• Sentiment Analysis
• Introduction to Computer Vision
• Image Processing Basics
• Image Classification
• Convolutional Neural Networks
• Real-World AI Applications

Module 12: Generative AI
• Introduction to Generative AI
• Large Language Models
• Generative AI Applications
• Prompt Engineering
• Working with AI APIs
• Text Generation
• Embeddings and Vector Search
• Retrieval-Augmented Generation Basics
• Building AI-Powered Applications

Module 13: Model Deployment & MLOps Basics
• Saving and Loading ML Models
• Building ML APIs
• Introduction to Flask/FastAPI
• Model Deployment Basics
• Introduction to Docker
• Model Monitoring Basics
• Version Control with Git
• Deploying AI/ML Applications

Module 14: Real-World AI & ML Projects
• House Price Prediction
• Customer Churn Prediction
• Customer Segmentation
• Sales Forecasting
• Sentiment Analysis Application
• Image Classification Project
• Recommendation System
• AI Chatbot
• Final End-to-End AI/ML Project

Module 15: Career Preparation
• AI & ML Interview Questions
• Python Interview Preparation
• Machine Learning Interview Preparation
• Data Science Case Studies
• ML Model Evaluation Questions
• AI Project Presentation
• GitHub Portfolio Preparation
• AI/ML Resume Preparation
• Building an AI/ML Portfolio
• Internship and Job Preparation