Foundation Course On Ai, Machine Learning And Generative Ai

Master AI, ML, and LLM to Build Intelligent Applications and Accelerate Your Career From Foundations to Advanced Agents

What you'll learn

Understand core concepts of Artificial Intelligence and Machine Learning including supervised and unsupervised learning.

Build and evaluate machine learning models using Python and libraries like scikit-learn, TensorFlow, and PyTorch.

Apply AI concepts to real-world problems through guided hands-on projects and hackathon challenges.

Master foundational tools and techniques to transition into an AI/ML career confidently, even with an IT background.

Requirements

Basic knowledge of programming concepts is helpful but not mandatory. Curiosity, commitment, and a willingness to learn are all you need — everything else will be taught from scratch.

Description

What you'll learn (Key takeaways):Develop a strong understanding of fundamental AI/ML principles and the role of LLMs within the AI ecosystem.Explore different types of machine learning, including Supervised, Unsupervised, and Reinforcement Learning, with real-world examples.Understand core machine learning concepts like model lifecycle, overfitting, loss functions, and evaluation metrics.Gain proficiency in Natural Language Processing (NLP) essentials and text representation techniques.Master advanced prompt engineering techniques, including Zero-shot, Few-shot, Role, Persona, and Chain-of-Thought prompting.Learn to integrate LLMs into applications using orchestration frameworks like LangChain.Understand and utilize vector databases for enhanced retrieval in LLM applications.Implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and reduce hallucinations in enterprise applications.Discover how LLMs can interact with external systems through tool and function calling.Build LangChain agents that leverage external tools to automate workflows.Delve into advanced agent design patterns and implement robust memory mechanisms for intelligent agents.Understand deployment strategies for LLM applications on cloud platforms, including containerization and serverless options.Learn key metrics and best practices for monitoring LLM performance and cost.Gain knowledge of ethical AI, responsible LLM development, and security best practices for LLMs.Explore advanced generative AI for code generation, data analysis, visualization, and creative content.Understand advanced RAG architectures like multi-hop and self-correcting RAG.Design complex, autonomous multi-agent systems and implement human-in-the-loop strategies.Develop practical skills by building real-world applications and managing your GitHub and LinkedIn profiles.Gain hands-on experience with tools like Gemini, OpenRouter, DeepSeek, Kimmy, Minimax, Genpark, and Qwen.Learn to generate applications quickly using AI tools and understand the evolving role of developers.Who is this course for?Developers and technical professionals looking to build intelligent applications using AI/ML and LLMs.Individuals interested in understanding the fundamentals and advanced concepts of AI, Machine Learning, and Deep Learning.Content creators and freelancers who want to leverage AI tools for their work.Anyone with a business idea looking to build applications without extensive technical background (vibe coding).Those seeking to enhance their career prospects in the rapidly evolving AI and ML landscape.Participants are recommended to have basic Python programming skills and familiarity with the command line interface.

Overview

Section 1: Introduction to AI and ML

Lecture 1 Introduction

Lecture 2 Artificial Intelligence History, Evolution and Current trend

Section 2: Fundamentals of Data, Machine Learning and Artificial Intelligence

Lecture 3 Python Basics

Lecture 4 Overview of AI and ML - History, Advancements, Practical applications

Section 3: Data pre-processing with Python for AIML

Lecture 5 Exploratory Data Analysis with Python Libraries Numpy, Pandas and Seaborn

Lecture 6 Fundamentals of Python for AI andML

Section 4: Foundations of Data, Math and Machine Learning Algorithms

Lecture 7 Machine Learning Deep Dive

Lecture 8 EDA, Probability, ML vs DL, Cursor, Model Context Protocol

Section 5: Foundational concepts of Machine Learning

Lecture 9 Machine Learning - Deep Dive - Types, Applications and related tools

Lecture 10 Machine Learning Models - Building and Evaluation

Section 6: Machine Learning - Deep Dive

Lecture 11 Overfitting, Underfitting, Model Evaluation & Regularization

Lecture 12 Dimensionality Reduction (PCA), Model Evaluation Metrics

Section 7: Machine Learning Recap, Deep Learning Fundamentals

Lecture 13 Machine Learning Recap - Deep Learning Fundamentals - Image Processing example

Lecture 14 ML vs DL, Deep Learning Concepts - Neural Networks

Section 8: Deep Neural Networks Deep Dive - CNN, RNN, LSTM

Lecture 15 Neural Networks - Deep Dive - CNN, RNN and LSTM

Lecture 16 Neural Networks - CNN In depth

Section 9: Neural Networks - CNN, RNN Deep dive - Generative AI (NLP, Transformers)

Lecture 17 Convolutional Neural Networks (CNN)

Lecture 18 Generative AI - NLP, Transformers

Section 10: Generative AI - NLP, AI Agents, Practical Applications

Lecture 19 Generative AI - NLP, AI Agents and Stock Market Prediction application example

Lecture 20 Generative AI - Large Language Models, AI Agents, RAG chatbot implementation

Section 11: LLMs Deep Dive, AI Agents implementation, Model Context Protocol (MCP)

Lecture 21 LLM deep dive, CrewAI Agentic Framework implementation, MCP Understanding

Lecture 22 AI Agents implementation, Cursor and CrewAI Framework

Lecture 23 Resume Review, LinkedIn network, Interview Preparation, Job Search - Evolving AI

This course is ideal for IT professionals, fresh graduates, and tech enthusiasts who are curious about Artificial Intelligence and Machine Learning. Whether you're switching careers, upskilling, or preparing for your first AI job, this course gives you a practical, beginner-friendly introduction without needing prior experience in AI/ML.