
Welcome to the "Artificial Intelligence, ML, AI-Agents A-Z with Python" course!!!
In this course I prioritize, understanding and applying core AI and ML concepts along with hands-on development.
Will cut through mathematical complexity by explaining theories in simple English, so that the knowledge "deeply seeps in".
I have 18+ years of experience in world-leading companies like IBM and HPE, I bring in substantial amount of practical expertise and industry insight into the course. I am certified in Data Science and Machine Learning from IIT Kanpur, India's premier technological institute. With all this, I bring in clear understanding of concepts and hands-on sessions, in line with industry expectations.
Whether you're a beginner or a developer transitioning into AI, this course offers real skills, industry-relevant knowledge, and up-to-date content, including ongoing project additions and practical sessions. This along with clear understanding prepares you for big opportunities.
Explore why AI is everywhere and why learning AI now leads to future-proof, high-paying roles while building projects like chatbots, data dashboards, and smart robots.
Explore the AI and ML ecosystem, from supervised and unsupervised learning to deep learning, reinforcement learning, and policy-based decision making, with core concepts like states, actions, rewards, and Bellman equation.
Explore how policy and action drive reinforcement learning, showing policy as a state-to-action rule and action as the execution, with deterministic and stochastic variants and learning-driven policy updates.
Explore the foundations of reinforcement learning, focusing on state, value, and policy, and see how their interaction optimizes agent behavior in complex environments.
Explore the Bellman equation and its role in dynamic programming and reinforcement learning, including the state value function and Bellman optimality, and applications in value iteration and TD methods.
Explore how the state value function and the action value function quantify expected returns in reinforcement learning, using a policy and gamma discount, illustrated with a grid world.
Classify reinforcement learning techniques into state value, action value, and policy-based methods. Learn dynamic programming, Monte Carlo, temporal difference, Q-learning, SARSA, reinforce, and actor-critic with TRPO and PPO.
Q-learning estimates a Q function to evaluate action values in states and updates them via the td error with alpha and gamma. It covers agent–environment interaction, states, actions, and rewards.
Implement q-learning to train an agent in a 4x4 maze using epsilon-greedy exploration and TD updates, with matplotlib-driven animations showing q-values and learned policies.
Implement q-learning in a 4x4 maze using epsilon-greedy exploration, td target, and q-value updates with alpha and gamma to navigate traps toward the goal.
Understand how large language models are built from tokens, embeddings, and weights, through transformer architecture with multi-head attention and positional encoding, supported by parameters, biases, and training methods.
Explore LLM components and learning mechanisms, including causal language modeling, masked language modeling, sequence-to-sequence, and RLHF, and learn optimization, decoding, hardware scaling, evaluation metrics, and safety alignment.
Dissect transformers by detailing input representation, tokenizing, layer normalization, residual connections, self-attention, multi-head attention, feed-forward networks, stacking, and the output layer producing vocabulary probabilities via softmax.
Explore how transformers represent text input through tokenization into words or subwords, embedding layers that map tokens to dense vectors, and positional encodings that preserve word order.
unlock self-attention in transformers by computing query, key, and value vectors to produce a context-aware word representation through attention scores, softmax, and weighted sums.
Explore how transformers enhance self-attention through multihead attention and a per token feedforward network. Learn how multiple heads capture grammar and semantics, and why nonlinear FFNs deepen representations.
Explore how transformers use residual connections and layer normalization to stabilize training, enable gradient flow, and, through stacked attention and feed-forward layers, excel at translation and classification.
Discover how transformers learn language through forward propagation, loss calculation, backward propagation, and optimization, while leveraging self-attention, embeddings, and multi-head layers for fast, scalable learning.
Explore embedding models that convert words, images, and audio into dense vectors, enabling dimensionality reduction, semantic understanding, and fast similarity computations for NLP, computer vision, and recommendations.
Learn the five embedding model types—word embeddings, sentence and document embeddings, contextual embeddings, item embeddings, and multimodal embeddings—and how they power NLP tasks, recommender systems, and cross-modal understanding.
Explore embedding models as the foundation of AI and NLP pipelines, mapping data to the embedding space via input processing, training objectives, and cosine similarity.
Explore embedding models and how they compare to ML models like CNNs and RNNs. Learn how embeddings power search, chatbots, translation, image search, recommendations, and fraud detection.
Explore prompting and prompt engineering to guide large language models. Learn zero short prompting, one short prompting, and few short prompting.
Master one-shot, few-shot, and chain-of-thought prompting to guide AI with single or multiple examples and explicit reasoning steps. Apply these techniques to translation, classification, and multi-step tasks to improve accuracy.
Explore prefix prompting to inject structured rules at the prompt start and achieve organized outputs. Learn json output prompting to produce machine-readable, structured data for apps and apis.
Master role-based and instruction-based prompting to tailor ai responses with the right tone, vocabulary, and explanation level for any audience, applying specificity, iteration, and examples.
Create and activate a Python virtual environment for the ai agent sdk prediction, install stock data and sentiment-analysis packages (yfinance, numpy, pandas, matplotlib, scikit-learn, requests, vadr sentiment), and run test.
Develop the first artificial intelligence agent step by fetching stock data with yfinance, using a 6-month default period, and set up sentiment analysis to later predict prices.
Extracts and numericalizes stock date indices to create model-ready features, using a prepareData function to return date num and close values as numeric x and y from a pandas DataFrame.
Learn to build stock price predictions using numpy and matplotlib, define a prediction function, prepare data from YFinance, generate 30 future dates, train a model, and plot the results.
Fetch the top five headlines from the news API using an API key and Python, then parse the JSON response and print the headlines for price prediction.
Develop an AI agent that predicts stock prices by fusing historical data from yfinance with Vader sentiment analysis of top headlines, and training a linear regression model.
Launch the first AI agent that fetches data from yfinance, applies sentiment analysis to headlines, and makes data-driven predictions, contrasting with traditional programs.
Interested in Machine Learning & AI Agents – understanding & Practice? Then this course is for you.
This course has been designed by me having 18+ years of experience in the industry designing & developing software & firmware for companies like IBM & HPE. I am certified from IIT – Kanpur, India’s Premier Technological Institute, on Data science.
In this course I have made sure that focus is on Understanding & Applying AI, ML, AI-Agent concepts & hands-on, which is the major Key in this astronomically growing field of AI & Machine Learning. There will be rarely any field that will be untouched by AI & ML very soon. Making sure we are on top of the latest with hands-on, is of utmost importance.
I will be walking you through the fascinating world of AI, ML, LLMs & AI-Agents with, so that the understanding seeps deep in, which is what the industry looks for & rewards.
I am a practitioner, very keen working on building a community which can drastically, transform existing tech-infra & build newer ones solving real-world problems, which matter. This course will enable you to think from creating newer systems or dramatically enhancing existing ones.
We will be using Python during the course. I will also be covering essential Python programming along the way.
What you’ll Learn:
Learn AI, ML & Build Real-World AI Agents from the Ground Up
Whether you're a beginner or an experienced developer stepping into the AI world, this course is designed to give you a strong foundation in AI, Machine Learning, and hands-on AI Agent development—taught from the perspective of a passionate corporate AI practitioner and educator.
Getting Started: Understand the Ecosystem – AI, ML, Data Science & Robotics
We’ll begin by demystifying the core concepts of Artificial Intelligence (AI), Machine Learning (ML), Data Science, and Robotics. Learn how these fields intersect, differ, and support each other.
You’ll also explore key foundational ideas like Policy, State, Value, and Action, which are the basic building blocks of intelligent decision-making in AI systems—especially important when designing AI agents.
Mathematics & Theories – Explained in Simple English
Don’t worry if you're not a math wizard! Complex theories are broken down in plain, beginner-friendly terms:
· Markov Decision Processes (MDPs) – the framework behind decision-making models in AI.
· Bellman Equation – how future rewards are estimated and optimized in RL systems.
· Q-Learning & Reinforcement Learning – the logic behind how agents "learn" by trial, error, and rewards.
Understanding these core ideas helps you to create intelligent systems that adapt, learn, and improve over time.
LLMs & Transformers – The Brains Behind Modern AI
Dive into the technology behind large language models (LLMs) like ChatGPT and BERT. Learn:
· What Transformers are, and how they revolutionized AI.
· How Self-Attention Mechanisms enable LLMs to understand context and nuance.
· A deep breakdown of key components:
o Input Representations
o Self-Attention & Multi-Head Attention
o Feedforward Neural Networks (FFN)
o Layer Normalization & Softmax
This section will give you a strong grasp of the architecture that powers today’s most powerful AI systems.
Embedding Models – Making Language & Data Machine-Understandable
You’ll learn why embedding models are critical for translating raw data (like text, images, and actions) into a form that AI models can understand. Will be covering:
· The need for embeddings in modern AI
· Different types of embedding models
· How embeddings work and how they fuel understanding in LLMs and agents
Understanding embeddings is key for mastering AI applications like search, recommendation systems, and conversational agents.
Prompting – The Art of Talking to Generative AI
Prompting is a core skill in working with LLMs today. This module focuses on:
Types of prompting (zero-shot, few-shot, chain-of-thought, etc.)
Techniques to optimize responses, extract accurate information, and guide the model effectively
Best practices for practical and efficient use of LLMs for automation, content generation, coding assistance, and more
AI Agents – Theory and Hands-On Development
Then, bringing it all together to understand what AI Agents are - that can autonomously perceive, reason, and act. You’ll gain:
· A strong theoretical understanding of how AI agents function
· Practical experience in building working AI agents step-by-step
· Insights into how agents use embeddings, transformers, and RL concepts to operate intelligently in real environments
Whether you're aiming to automate tasks, build conversational bots, or create decision-making systems, this module will empower you to build agents that don’t just respond - they think & act.
Course Updates & Expansion
I’m committed to keeping this course dynamic and up-to-date. In the coming weeks:
· More hands-on AI agent projects will be added
· Additional theoretical sessions on emerging topics in AI and ML will be included
· I’ll also be incorporating student suggestions wherever relevant to make sure your learning needs are addressed
Who this course is for:
Anyone interested in AI, Machine Learning, & AI Agents.
Aspiring AI & ML Beginners: Those new to AI who want a clear, jargon-free introduction to core concepts, including reinforcement learning and transformers.
Software Developers & Engineers: Professionals looking to expand their skill set with practical AI/ML and real-world project experience.
Business Executives and Managers: Professionals in leadership roles who are looking to deepen their understanding of embedding models, prompting strategies, and large language model functioning.
Corporate & Enterprise Professionals: Team leads, managers, or consultants seeking insights from a seasoned AI practitioner on best practices, pitfalls, and scalable agent architectures.
Career Changers & Students: Anyone aiming to change your career path and get into AI field with hands-on labs, portfolio-worthy projects, and a modern understanding of AI agent development.
AI Enthusiasts & Innovators: Anyone excited by the “greatest revolution since the internet boom” and eager to build, deploy, and iterate on autonomous AI agents.
Students who have at least high school knowledge in math and wanting to start a career in AI, Machine learning.
Anyone who wants to add value to their career or business by using powerful Machine Learning tools.
Why Take This Course?
This course is not just about watching videos - it’s about building your AI intuition, mastering real-world tools, and preparing to contribute meaningfully to modern AI projects. If you're eager to understand and build the technology behind the AI revolution, this course is for you.
Get started on the journey of learning, building, and innovating…
Do you think you don’t have the math or coding background.
This course breaks down even advanced topics like Markov Decision Processes, the Bellman equation, and Q-Learning in plain English, with step-by-step demos. You’ll build intuition before diving into formulas, so no prior AI experience is required.
I won’t have time - online courses are a huge time-sink and many students drop out.
Research shows that “too much time required” and “lectures that drag on” are top reasons for drop-outs. That’s why this course is organized into short, focused modules, plus bite-sized hands-on labs, so you can learn and apply concepts in under an hour even on a busy schedule.
Most courses are all theory where’s the practical, real-world work?
The course balances important theoretical lessons with coding exercises and career-ready projects. You’ll not only learn LLM internals and embedding math, but also be able to build functional AI agents.
Thinking – If this will help with your career?
Employers look for demonstrable skills. With this, you’ll have projects worth demonstrating - starting with simple AI agent-based ones to complex ones - that substantiates your learning and practice. Plus, as an industry-experienced practitioner, I share best practices and pitfalls to help you hit the ground running in enterprise environments.
Enrol today and jump on to the fastest-growing fields of AI, ML, and AI-Agent development - your future self will thank you!
Grab the greatest opportunity since the 1990s’ internet boom: opportunity like this takes decades to come by, jump in early to lead the way.
Learn to build real-world AI projects with expert-led hands-on sessions, stand out in a competitive market, and stay ahead of the curve.