This module will teach you the most important libraries and techniques for analyzing, cleaning, and visualizing data in Python. You'll gain hands-on experience working with real datasets to discover insights and present your findings.

Data Analysis with NumPy:

Data Manipulation with Pandas:

Data Visualization with Matplotlib:

Advanced Visualization with Seaborn:

Interactive Dashboards with Plotly:

By completing this module, you'll be able to confidently prepare, analyze, and visualize data using Python's most powerful libraries.

This module introduces the fundamentals of image processing and computer vision using Python. Students will gain hands-on experience with the OpenCV library to process images and build real-world vision-based applications.

Image Processing Fundamentals:

Image Enhancement & Filtering:

Face Detection:

Face Recognition:

Tools & Libraries Used:

By the end of this module, students will be able to build complete face detection and face recognition systems using real-world data and live camera input.

Learn the fundamentals of supervised machine learning, covering regression, classification, and practical model evaluation techniques.

Core ML Concepts:

Model Evaluation:

Tools Used:

Mini Projects:

By completing this module, you'll be able to build and evaluate supervised ML models for real-world datasets.

Master unsupervised learning techniques to group similar data points, reduce dimensions, and prepare features for better model performance.

Core Unsupervised Learning Concepts:

Tools Used:

Mini Projects:

By completing this module, you'll gain the skills to analyze, group, and simplify data without predefined labels.

Learn the foundations of deep learning, neural networks, and essential concepts for building AI models from scratch.

Core Deep Learning Concepts:

By completing this module, you'll understand how deep learning works and be able to implement basic neural networks.

Advanced Deep Learning Concepts:

Tools Used:

Mini Projects:

By completing this module, you'll be equipped to work on advanced AI applications and deploy deep learning models effectively.

Learn to process and analyze text data for building intelligent NLP applications. Master text preprocessing, feature extraction, and classification techniques.

Core NLP Concepts:

By the end, you'll be able to transform raw text into structured features and build NLP models for classification and analysis.

Tools Used:

Mini Projects:

Apply NLP techniques to real-world datasets, build sentiment prediction models, and automate text classification tasks.

Understand the core concepts of Generative AI and Large Language Models (LLMs). Learn prompt engineering, embeddings, and how to integrate AI APIs into applications.

Core GenAI Concepts:

By the end, you'll be able to design prompts, store embeddings, and integrate LLMs into your applications.

Tools Used:

Learn to build real-world Generative AI applications from scratch. Explore LangChain, LlamaIndex, RAG pipelines, and deploy apps with modern tools.

Core GenAI App Concepts:

By the end, you'll know how to connect LLMs with custom data and deploy interactive AI applications.

Tools Used: