Machine Learning Basics: You will start with an in-depth understanding of supervised and unsupervised learning. Learn how to build models for classification, regression, clustering, and dimensionality reduction using popular frameworks like Scikit-Learn and TensorFlow. Detailed examples and hands-on exercises will solidify your understanding of these core concepts.
Practical projects > Developing a spam email classifier and a Customer segmentation model for targeted marketing.

Neural Networks: Explore the architecture and functioning of neural networks. This section will cover feedforward networks, backpropagation, activation functions, and the importance of neural network training. You will gain practical experience by building and optimizing neural networks using TensorFlow and Keras, understanding how to handle overfitting and underfitting.
Practical projects > Creating a digit recognition system using MNIST dataset and a Predictive model for stock prices.

Computer Vision: Delve into computer vision techniques, including image processing, convolutional neural networks (CNNs), and object detection. Learn how to implement image recognition models using OpenCV and TensorFlow. This section will also cover advanced topics like transfer learning and real-time image processing applications.
Practical projects > Developing an object detection system for security cameras and a Facial recognition application for user authentication.

Natural Language Processing (NLP): Understand the fundamentals of NLP, including text preprocessing, tokenization, and sentiment analysis. Explore advanced NLP models such as transformers and BERT. Practical sessions will guide you through building NLP applications for tasks like language translation, text summarization, and entity recognition.
Practical projects > Building a sentiment analysis tool for social media data and a Language translation app using transformer models.

Generative AI: Learn about the principles of Generative AI and how it works. This section will cover various generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). You will understand how these models generate new data and their applications in creating synthetic images, music, and text.
Practical projects > Generating synthetic images using GANs and Creating a music composition AI using VAEs.

Prompt Engineering: Discover the art of prompt engineering to fine-tune LLMs for specific tasks. Learn how to craft effective prompts to guide AI behavior and improve model outputs. Practical examples will demonstrate how prompt engineering enhances the performance of models like GPT-4.
Practical projects > Developing a question-answering system and Creating a chatbot for customer support.

Building LLM Applications: Explore the development of applications using LLMs. This includes integrating LLMs with web and mobile applications, creating conversational agents, and implementing features like text generation and summarization. Hands-on projects will give you the experience needed to build real-world LLM applications.
Practical projects > Building a content generation tool for blogs and a Summarization tool for lengthy documents.

Retrieval-Augmented Generation (RAG) and LangChain: Learn how to combine retrieval-based and generation-based models to improve response accuracy. This section will cover the implementation of RAG pipelines and the use of LangChain for managing complex AI workflows.
Practical projects > Developing a personalized recommendation system and an AI-powered search engine.

Fine-Tuning and Deploying LLMs: Understand the process of fine-tuning pre-trained models to specific datasets. You will learn best practices for deploying LLMs in production environments, including model optimization, monitoring, and maintenance.
Practical projects > Fine-tuning an LLM for a specific domain and Deploying it as a web service.

Building Bot Agents: Gain insights into creating intelligent bot agents that interact with users naturally. This includes integrating AI models with chatbot frameworks, designing conversation flows, and deploying bots on various platforms like Slack, Facebook Messenger, and custom websites.
Practical projects > Developing a customer service chatbot and an Interactive educational assistant.