Course Resources & Materials

Access comprehensive learning materials, project briefs, and recommended readings to support your AI journey.

Downloadable Study Materials

Essential guides and reference materials available for enrolled students.

Complete Course Syllabus

Detailed week-by-week breakdown of all topics and projects

PDF • 2.3 MB

Python Programming Cheat Sheet

Essential Python syntax and functions for AI development

PDF • 1.8 MB

Machine Learning Algorithms Guide

Comprehensive guide to classical ML algorithms with examples

PDF • 4.5 MB

Deep Learning Mathematics Reference

Linear algebra and calculus concepts used in deep learning

PDF • 3.1 MB

Neural Networks Architecture Guide

Visual guide to CNN, RNN, and Transformer architectures

PDF • 5.2 MB

NLP Preprocessing Techniques

Step-by-step guide to text preprocessing for NLP tasks

PDF • 2.7 MB

Note: Full access to all materials is granted upon enrollment. Sample materials available for prospective students.

Recommended Reading

Essential books and papers to deepen your understanding of AI concepts.

Core Textbooks

Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron Courville

Textbook

Comprehensive introduction to deep learning fundamentals

Pattern Recognition and Machine Learning

Christopher Bishop

Textbook

Mathematical foundations of machine learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Aurélien Géron

Practical Guide

Practical implementations and real-world examples

Natural Language Processing with Transformers

Lewis Tunstall, Leandro von Werra, Thomas Wolf

Textbook

Modern NLP with transformer architectures

Artificial Intelligence: A Modern Approach

Stuart Russell, Peter Norvig

Textbook

Comprehensive AI fundamentals and theory

The Hundred-Page Machine Learning Book

Andriy Burkov

Quick Reference

Concise overview of ML concepts and algorithms

Key Research Papers

Attention Is All You Need

NeurIPS 2017

Original transformer architecture paper

ImageNet Classification with Deep CNNs

NeurIPS 2012

AlexNet paper that revolutionized computer vision

BERT: Pre-training of Deep Bidirectional Transformers

NAACL 2019

Foundation of modern NLP models

Project Briefs

Hands-on projects to build your AI portfolio and demonstrate your skills.

Week 3

Predictive Housing Price Model

Build a regression model to predict housing prices using classical ML algorithms

Skills Covered:
Data preprocessingFeature engineeringLinear regressionModel evaluation
Deliverables:
  • Jupyter notebook
  • Model performance report
  • Feature importance analysis
Week 5

Image Classification System

Create a CNN-based image classifier for a custom dataset

Skills Covered:
Convolutional networksTransfer learningData augmentationModel optimization
Deliverables:
  • Trained model
  • Web interface
  • Accuracy comparison report
Week 7

Stock Price Prediction

Develop an LSTM network to forecast stock prices using historical data

Skills Covered:
Time series analysisLSTM networksData normalizationSequence modeling
Deliverables:
  • LSTM model
  • Prediction visualizations
  • Model evaluation report
Week 9

Intelligent Chatbot

Build a conversational AI chatbot using transformers and LangChain

Skills Covered:
NLPTransformersContext managementAPI integration
Deliverables:
  • Working chatbot
  • Conversation logs
  • System architecture document
Week 10

Real-Time Object Detection

Implement a real-time object detection system using YOLO or similar architecture

Skills Covered:
Object detectionComputer visionReal-time processingModel deployment
Deliverables:
  • Detection system
  • Demo video
  • Performance benchmarks
Week 12

Capstone Project

End-to-end AI project of your choice, deployed to production

Skills Covered:
Full ML pipelineCloud deploymentMLOpsSystem design
Deliverables:
  • Production deployment
  • Documentation
  • Presentation
  • GitHub repository

Tools & Software

All the tools you'll use during the program. Most are free and open-source, and we provide cloud credits for paid services.

Programming & Development

Python 3.9+Free

Primary programming language

Jupyter NotebooksFree

Interactive coding environment

VS CodeFree

Code editor with AI extensions

Git & GitHubFree

Version control and collaboration

Machine Learning Frameworks

TensorFlow 2.xFree

Deep learning framework by Google

PyTorchFree

Deep learning framework by Meta

Scikit-learnFree

Classical ML algorithms

KerasFree

High-level neural networks API

Data Processing & Analysis

NumPyFree

Numerical computing

PandasFree

Data manipulation and analysis

Matplotlib & SeabornFree

Data visualization

OpenCVFree

Computer vision library

Cloud & Deployment

Google ColabFree

Cloud Jupyter notebooks with free GPU

AWS / GCP / Azure

Cloud computing platforms (credits provided)

DockerFree

Containerization platform

Hugging FaceFree

Model hub and transformers library

Computing Resources Included

We provide $500 in cloud computing credits (AWS/GCP/Azure) for GPU training and model deployment. All software and tools used in the course are free or have free tiers for students.