AI MasterClass

AI MasterClass — Become an Industry-Ready AI Engineer. Go from Python basics to cutting-edge Generative and Agentic AI in a focused 6-month journey. Learn to design, build, and deploy real AI systems using modern tools and frameworks trusted by top tech teams.

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What You'll Get

Course Overview

Artificial Intelligence

Generative AI

Agentic AI

Machine Learning

Deep Learning

Python

Mock Interview Preparation

Profile Enhancement

Topic Wise Assessment

Real World Use Cases

What Will You Learn?

Build Python skills from fundamentals to industry-ready.
Master Machine Learning, Deep Learning & Generative AI concepts.
Learn to create Agentic AI and Multi Agent systems from scratch.
Work on 4 real-world, industry-aligned use cases.
Validate learning through daily & monthly evaluations.
Master the skill of showcasing your project work to impress interviewers.
Get biweekly live doubt-clearing sessions.
Access recorded session videos for flexible learning.
Join an exclusive WhatsApp community for learning & collaboration.

Build the core skills of an AI scientist — deep data understanding, choosing the right model for the right problem, feature engineering, and turning data into meaningful business insights.

Ideal for — Beginner and Intermediate level.

Career Paths: Data Scientist • ML Engineer • GenAI Engineer

Course Duration — 6 Months

Curriculum

Course Content

A structured, module-by-module journey from Python fundamentals to Agentic AI — with assessments and projects at every stage.

Program Highlights

2 Mock Interviews 4 Module-wise Evaluations 6 Real-world Use Cases Final Capstone Project 4 Self-Paced / Group Assignments Daily Topic-wise Assessments

Initial Knowledge Assessment

Module 1

Python for Data Science

Python Fundamentals

Variables & data types, Loops & conditionals, Functions & lambda, Basic file handling

Numerical Computing (NumPy)

NumPy arrays, Indexing & slicing, Vectorized operations, Statistical functions

Pandas – Data Analysis

Series & DataFrames, Reading data (CSV, Excel, JSON), Data cleaning, Filtering & grouping

Data Visualisation

Line, bar, scatter, histogram, box plots, Chart customization, Styling & themes

Project: Data Visualisation and Analytics

Assessment: Python Evaluation — A coding round equivalent

Module 2

Machine Learning Foundations

Statistics Basics

Central Tendencies, CLT, Data Distributions, Hypothesis Testing

ML Foundations

Feature Engineering, Outlier Detection, Bias-Variance Tradeoff, Overfitting & Underfitting

Supervised Learning

Predictive & Descriptive Analytics, Classification, Regression, Regularization

Unsupervised Learning

Clustering, Recommendation Engine

Time Series Analysis

Time Series Forecasting techniques

Project: Time Series real-world problem

Assessment: ML & Statistics — A technical round equivalent

Module 3

Deep Learning Essentials

Network Basics

Activation Functions, Optimisation Algorithms, ANN

Computer Vision

CNN, Image Classification, Segmentation, Object Detection, CNN Architectures

NLP

Text Preprocessing, Embeddings, RNN, GRU, Transformer Architectures

Project: NLP / Computer Vision use cases

Assessment: Deep Learning — Use case based deep dive

Module 4

Generative AI & LLMs

GenAI Foundations

LLM types, Open source vs closed source models

Prompt Engineering

Zero-shot, Few-shot, Chain-of-thought

Open Source Model Hosting

HuggingFace, GPU basics, Quantization, FastAPI, LangChain

GenAI Safety

Responsible AI, Guardrails, Evaluation Strategies

Assessment: Generative AI & its applications

Module 5

RAG Applications

RAG Systems

Introduction to RAG, Data Ingestion from PDF, TXT, Word

Vector Databases & Embeddings

Vector DBs, Embedding Models

Advanced RAG

Pre-retrieval & post-retrieval strategies, Re-ranking, Evaluation

Project: End-to-end RAG solution + Chatbot use case

Module 6

Agentic AI & Multi-Agent Systems

Foundations

AI Agents, Reflection, Reflexion, ReAct

Agentic Components

Tool calling, Function calling, Memory

Pre-built & Multi-Agent

LangChain Agents, LangGraph, CrewAI

Project: Real-world Agentic AI use case + Final Capstone

Final Capstone Project — Presentation & Evaluation

Mock Interview 2

Additional Classes

Project Presentation Skills Resume & Portfolio Development Bi-Weekly Live Doubt Clearing Coding Round Preparation

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Register Your Interest

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FAQ

Frequently Asked Questions

This course is ideal for students with basic to intermediate level of AI knowledge.

The AI MasterClass course is a deep dive of entire AI foundations. It will help you build strong fundamentals from Statistics to GenAI which makes it easier to build and develop any AI solution.

The Initial Knowledge Assessment is the first round of evaluation conducted by TheAIClan to understand your knowledge levels.

The result of the assessment would be a detailed report containing detailed analysis about your areas of excel and area of improvement in your AI journey. It also classifies your knowledge in beginner, intermediate and advanced level.

There are three types of evaluation: 1. Daily Topic Wise Assessment (DTWA) — conducted at the beginning of every class. 2. Module Completion Assessment (MCA). 3. Mock Interviews.

Yes, Daily Topic Wise Assessment (DTWA) and Module Completion Assessment (MCA) will be MCQ based. MCA will be a detailed 1-hour assessment.

This is an industry-simulating interview — a personalised 1-on-1 session which will have both coding and technical questions. A detailed review would be shared over mail on areas of improvement and areas of excel.

These are assignments given to individuals in the class. They can choose to either work in groups or individually. We facilitate group creation in the class.