Syllabus - AI & Machine Learning

This syllabus covers the foundations of Artificial Intelligence and Machine Learning, exploring real-world applications, algorithms, and tools necessary for building intelligent systems.

Session 1 – Introduction to AI & ML

  • What is AI and Machine Learning?
  • Types of Machine Learning
  • Applications of AI in Industry
  • AI vs ML vs Deep Learning
  • Overview of AI Tools and Platforms

Session 2 – Python for Machine Learning

  • Python Basics for AI/ML
  • NumPy, Pandas, and Matplotlib
  • Jupyter Notebooks for Code & Output
  • Working with Datasets

Session 3 – Data Preprocessing & Feature Engineering

  • Cleaning and Normalizing Data
  • Handling Missing Values
  • Encoding Categorical Variables
  • Feature Selection Techniques

Session 4 – Supervised Learning

  • Regression Algorithms (Linear, Polynomial)
  • Classification Algorithms (Logistic, k-NN, Decision Trees)
  • Model Evaluation Metrics
  • Hands-on ML Projects

Session 5 – Unsupervised Learning

  • Clustering Techniques (k-Means, Hierarchical)
  • Dimensionality Reduction (PCA)
  • Market Basket Analysis
  • Practical Use Cases

Session 6 – Introduction to Deep Learning

  • Neural Network Basics
  • Architecture: Input, Hidden & Output Layers
  • Activation Functions
  • Use of TensorFlow and Keras

Session 7 – Model Tuning and Validation

  • Train-Test Split & Cross-Validation
  • Overfitting & Underfitting
  • Hyperparameter Tuning (Grid Search)
  • Performance Evaluation

Session 8 – Real-world AI/ML Projects

  • Project Planning & Data Understanding
  • Building & Deploying ML Models
  • Use Case: Chatbot / Fraud Detection / Image Classifier
  • End-to-End Workflow Implementation

Session 9 – Capstone & Career Guidance

  • Capstone Project Execution
  • Presentation & Evaluation
  • AI/ML Job Roles & Resume Tips
  • Interview Preparation & Guidance