Live online courses in AI, machine learning and Python

Six programmes, from using AI tools well to building and deploying your own AI applications. Every session is live, two hours long, and everything is built in the session with you typing.

Live with a trainer, not pre-recorded
2-hour sessions, weekday evenings or weekends
Free tools, no paid subscriptions
A certificate that names what you built

Compare the courses Register

Pick a starting point

The first two need no background at all. Machine learning is taught before generative AI, because knowing how a model is trained makes the rest land properly.

Course	Length	You need	You will	Fee
AI Tools Mastery	6 sessions, 12 hrs	Nothing technical	Use AI well in your daily work	₹1,499
Python Programming	10 sessions, 20 hrs	Nothing	Learn to program properly	₹1,999
Machine Learning & Deep Learning	12 sessions, 24 hrs	Basic Python	Build and evaluate real models	₹2,999
Generative AI & LLM Development	15 sessions, 30 hrs	Basic Python	Build and deploy AI applications	₹3,499
Complete AI Programme	30 sessions, 60 hrs	Python basics, or take Python first	Go from beginner to job-ready	₹5,999
Internship Programme	15 days, 30 hrs	Nothing	Complete your internship requirement	₹2,499

No coding

AI Tools Mastery

₹1,499

Six sessions on using AI tools properly for writing, research, teaching material, slides, data and the tasks you repeat every week. Bring a real piece of your own work each time and leave with it finished.

6 sessions, 12 hours
2 hours a session
No technical background
Free tools throughout

Session-wise syllabus

Session	Topic	What you do
1	What the main assistants are each good at, and where each falls over. Writing a prompt that works first time. Spotting a confident wrong answer.	A prompt library for your own weekly work
2	Writing and editing: drafting, restructuring, tightening, and adapting one piece for different readers.	A document of yours, rewritten and compared
3	Reading and research: long PDFs, comparing papers, building a summary you can defend, and checking claims rather than trusting them.	A literature summary with sources verified
4	Slides and images: notes into a deck, images that are usable, teaching and promotional material made quickly.	A presentation built from your own notes
5	Data without code: asking questions of a spreadsheet, cleaning it, and making charts that answer something.	An analysis of your own data
6	Automating the repeats: the weekly tasks worth handing over, and setting one up end to end.	One automation, running

Suits: faculty, research scholars and working professionals whose week goes on writing, reporting and preparing material.

On using AI in academic work. Session 3 covers what is appropriate and what is not: AI as a reading, drafting and editing aid, not as an author, and what journals currently expect you to disclose.

Start here if you have never coded

Python Programming

₹1,999

From your first line of code to a working application. Every session ends with something that runs, and the course finishes with a project you deploy.

10 sessions, 20 hours
2 hours a session
No prior coding
Certificate

Session-wise syllabus

Session	Topic	What you build
1	Setting up Python and Colab. Syntax, variables, numbers and strings: indexing, slicing and the string methods you will actually use.	Your first working scripts
2	Lists, dictionaries, tuples and sets, and knowing which one a problem calls for. Reading and writing files.	A program that reads a file and reports on it
3	Decisions and repetition: conditionals, loops, and writing functions that do one thing well.	A data-processing script
4	Errors and objects: try and except, custom exceptions, then classes, objects, inheritance and the dunder methods.	An object-oriented library system with error handling
5	NumPy and pandas for real data: loading, cleaning, filtering, merging and grouping.	A messy dataset, cleaned and summarised
6	Matplotlib and Seaborn: charts that answer a question rather than decorate a slide.	An exploratory analysis with charts
7	The standard library worth knowing: collections, datetime, math, random, os. Regular expressions, and how to debug rather than guess.	A utility that solves a real problem of yours
8	The web: HTTP requests, working with APIs, parsing JSON, and web scraping, including where the legal lines are.	A scraper and an API client
9	Files and databases: CSV, Excel and PDF handling, basic image processing, and connecting Python to SQLite.	A script that processes files into a database
10	Putting it together: structuring a project, testing it, and deploying it so someone else can use it.	A complete application, deployed

Suits: school and college students, and professionals who need Python for data, automation, or as the foundation for the AI courses below.

Track 1

Machine Learning & Deep Learning

₹2,999

The models, and the judgement to use them. Twelve sessions from a first regression to convolutional networks, with as much time on evaluating a model honestly as on training one.

12 sessions, 24 hours
Basic Python needed
Scikit-learn, PyTorch, Keras
Certificate

Session-wise syllabus

Session	Topic	What you build
1	What machine learning is and is not. Supervised, unsupervised and reinforcement learning. The complete workflow, start to finish.	A first model, end to end
2	Data preparation: cleaning, scaling, encoding categories, and splitting data. Data leakage, and how it quietly ruins results.	A prepared dataset you can trust
3	Regression: linear regression, gradient descent, loss functions, and evaluating with MAE, RMSE and R².	A prediction model on real data
4	Classification: logistic regression, decision trees and k-nearest neighbours. Confusion matrices, precision, recall and F1.	A classifier, with its errors understood
5	Random forests and support vector machines, and where each one wins.	Two models compared on the same problem
6	Doing it properly: cross-validation, the bias-variance tradeoff, regularisation, hyperparameter tuning and ensembles.	A tuned model, and evidence the tuning helped
7	Unsupervised learning: k-means clustering, and what clusters do and do not tell you.	Customer segmentation on real data
8	Neural networks from first principles: neurons, layers, activations, forward propagation and backpropagation, built in NumPy.	A neural network written from scratch
9	PyTorch and Keras: the training loop, evaluation, and reading someone else's model code.	The same network, rebuilt in a framework
10	Convolutional networks and image classification. Transfer learning: using a trained model instead of starting over.	An image classifier on your own images
11	Sequences: recurrent networks and LSTMs for time series and text. Text processing and sentiment analysis.	A forecast and a sentiment model
12	Computer vision with OpenCV and YOLO: face detection and real-time object detection. Then deploying a model with Streamlit.	Object detection from a webcam, and a deployed model

Suits: students starting in AI or data science, scholars who need models for their research, and professionals moving into an AI role.

Track 2, most in demand

Generative AI & LLM Development

₹3,499

Fifteen sessions from a first API call to a deployed AI application with your name on it: prompting, LangChain, retrieval over your own documents, agents that use tools, and a capstone you can show an employer.

15 sessions, 30 hours
Basic Python needed
Gemini, Groq, LangChain, ChromaDB, Gradio
Deployed to Hugging Face Spaces

Session-wise syllabus

Session	Topic	What you build
1	How generative AI sits inside AI as a whole. What large language models are doing underneath. Calling the Gemini and Groq APIs.	Your first API call, and a simple chatbot
2	Python for AI development: functions, classes, object-oriented basics and handling errors that come back from an API.	A chatbot class that remembers the conversation
3	Prompt engineering: zero-shot, few-shot and chain-of-thought. Prompt templates, and building a library you reuse.	A tested prompt library
4	System prompts and conversational design. Gradio: turning a script into something another person can use.	An interactive AI web app
5	LangChain fundamentals: prompt templates, the expression language, and chaining steps together.	A multi-tab AI toolkit
6	Memory: buffer, window and summary memory, and choosing between them.	A chatbot that remembers across sessions
7	Retrieval-augmented generation: loading documents, embeddings, and ChromaDB.	A document question-answering system
8	Advanced RAG: PDF upload and processing, chunking strategies that work, and answers that cite their sources.	PDF Q&A with citations
9	Putting a RAG system together properly, with retrieval and generation as one architecture.	A four-tab study assistant
10	AI agents: what separates an agent from a chatbot. Tools, the tool decorator, and the reasoning loop.	A personal assistant agent
11	Agents with real tools: web and Wikipedia search, multi-tool workflows, and handling the errors agents make.	A research assistant agent
12	Multi-agent systems: the writer, critic and editor pattern, planning, and agents handing work between them.	A multi-agent workflow, and your capstone plan
13	Capstone, part one: project setup and the core features.	A working prototype
14	Capstone, part two: refining it and deploying to Hugging Face Spaces.	A live app anyone can open
15	Demo day. Presenting your project, adding it to your LinkedIn portfolio, and preparing for the interview questions these roles ask.	A demo, a portfolio entry, and interview preparation

Suits: students and professionals who want to build AI applications rather than only use them.

You finish with something to show. A live application on Hugging Face Spaces, a LinkedIn portfolio entry, and preparation for the questions these interviews ask. Most courses stop at the last topic.

Both tracks, plus a joint capstone

Complete AI Programme

₹5,999

Machine Learning & Deep Learning followed by Generative AI & LLM Development, taught in that order, and finishing with a joint capstone that uses both. Understanding how a model learns is what makes the second half land properly.

30 sessions, 60 hours
No prior AI experience
Three sessions you cannot book separately
Capstone, demo day and career preparation

How the programme runs

Part	Covers	Sessions
Track 1	Machine learning, model evaluation, neural networks, deep learning, computer vision and deployment: the full Machine Learning & Deep Learning syllabus above.	12 sessions
Track 2	Generative AI, prompt engineering, LangChain, RAG, agents and multi-agent systems: the full Generative AI & LLM Development syllabus above.	15 sessions
Joint capstone	A project that uses both halves: a model you trained, behind an AI interface people can talk to. Built, deployed and presented.	3 sessions

Suits: anyone going from no AI background to employable. It is also the version departments usually choose when running this as a value-added course.

Machine learning comes first on purpose. Going straight to LLMs without knowing how a model is trained and evaluated is how people build things they cannot debug. The final three sessions exist only here.

For your college requirement

Internship Programme

₹2,499

Fifteen days, two hours a day, ending with a project you build and present. Built for the internship your college asks for, with a certificate that names the dates, the topics and the work you did.

15 days, 30 hours
2 hours a day
A mini-project every day
Internship certificate

Day-wise plan

Day	Topic	Mini-project
Day 1	Introduction to AI, Google Colab and Python basics	Data preprocessing on a real dataset
Day 2	Introduction to machine learning, and the workflow end to end	Your first trained model
Day 3	Regression: linear and multiple regression	A prediction model on real data
Day 4	Decision trees and random forests	Tree-based models compared
Day 5	Classification algorithms	Logistic regression and random forest classifiers
Day 6	Unsupervised learning and clustering	Customer segmentation with k-means
Day 7	Reinforcement learning basics	A reward-based game agent
Day 8	Deep learning and neural networks	An ANN trained on a large dataset
Day 9	Convolutional networks for images	An image classifier
Day 10	OpenCV basics	Face detection
Day 11	OpenCV and YOLO	Live object detection from a webcam
Day 12	Natural language processing: text preprocessing and tokenisation	Sentiment analysis
Day 13	Generative AI and large language models	A working Gen AI environment, and a chatbot with Groq
Day 14	Project day: you build, with the trainer on call	Your chosen project, finished
Day 15	Presentation day	You present and demonstrate your project

Suits: students who need a documented internship. It also runs on campus for a whole class: see workshops for colleges.

Certificates name what you did. Dates, topics covered and the project completed, not only attendance. That matters when a placement officer or an interviewer reads it.

How every course runs

Live, small batches

A trainer teaching in real time. You can stop them and ask.

Two hours at a time

Long enough to build something, short enough to hold attention after a working day.

You type everything

No watching someone else's screen for two hours. Code runs in Google Colab, so any laptop works.

Recordings for revision

If you miss a session, you get the recording and the notebook the same day.

Help between sessions

A WhatsApp group for the batch, answered by the trainer.

You finish with proof

A project you built, and a certificate naming what you did.

Your trainers

Every session is taught live by one of the two people who also run MRL's college workshops.

Asborn T

AI, ML and IoT educator

16 years in technical education. Teaches artificial intelligence, machine learning, generative AI, large language models, agentic AI and IoT to engineering students, faculty and corporate teams.

Leads: Machine Learning & Deep Learning, Generative AI & LLM Development, AI Tools Mastery, and AI workshops.

Ranjith Kumar V

Full stack developer (Java) and IoT engineer, B.E. Computer Science

Three years at MRL Tech Solutions building and teaching real systems. Works across Java full stack development, robotics, the Internet of Things, Python, web development, and databases with SQL and MySQL.

Leads: Python Programming, and IoT, robotics and web development workshops.

Asborn working through a problem with a group of students at a computer — Asborn working through a model with students at Andhra Engineering College.

Ranjith showing students the wiring of a robot car kit — Ranjith taking students through a robot car build in an IoT workshop.

Colleges where we have taught

The same trainers and material, delivered on these campuses as workshops, value-added courses and internships.

Andhra Engineering College
DMI College of Engineering
Gojan School of Business and Technology
Kings Engineering College
New Prince Shri Bhavani College of Engineering and Technology
Panimalar Engineering College
Prince Shri Venkateshwara Padmavathy Engineering College
S.A. Engineering College
SRM University
Saveetha Engineering College
Saveetha University
Sri Manakula Vinayagar Engineering College
Sri Sai Ram Institute of Technology
St. Joseph College of Engineering
St. Joseph's College of Engineering
Valliammai Engineering College
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Andhra Engineering College
DMI College of Engineering
Gojan School of Business and Technology
Kings Engineering College
New Prince Shri Bhavani College of Engineering and Technology
Panimalar Engineering College
Prince Shri Venkateshwara Padmavathy Engineering College
S.A. Engineering College
SRM University
Saveetha Engineering College
Saveetha University
Sri Manakula Vinayagar Engineering College
Sri Sai Ram Institute of Technology
St. Joseph College of Engineering
St. Joseph's College of Engineering
Valliammai Engineering College
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Want this for your whole department? See workshops for colleges.

Register

Fill this in and press the button. It opens WhatsApp with your details written out. We reply with the next batch dates and payment details, usually the same day.

Questions people usually ask

Which course should I start with?

If you don't want to code at all: AI Tools Mastery. If you want to build but have never programmed: Python Programming first. If you can already write Python: Machine Learning & Deep Learning, then Generative AI & LLM Development, or the Complete AI Programme, which is both together plus a joint capstone.

Do I need a powerful laptop?

No. Code runs in Google Colab in your browser, so any laptop with a stable internet connection works. A phone is not enough for the hands-on work.

Will I need to pay for AI tools or API keys?

No. We teach on Gemini and Groq, both of which have free tiers generous enough for the whole course, and we show you how to watch your usage.

What if I miss a session?

You get the recording and the notebook the same day, and you can bring questions to the next session or the batch WhatsApp group.

Is the internship certificate accepted by colleges?

The certificate is issued by MRL Tech Solutions Pvt Ltd and names the dates, topics and project. Most colleges accept it for the internship requirement, but check your department's rules before you register.

Can our college book a course for a whole class?

Yes. Any of these runs as a batch for a department, online or on campus. See workshops for colleges.

Not sure which course fits?

Tell us what you know already and what you want to be able to do. We will suggest a starting point, honestly, including when the answer is a free resource instead.

Ask on WhatsApp Call +91 99624 19221