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Sirius Institute

Become the brightest star in the AI universe

A 4-month intensive placement-focused masterclass that takes you from zero coding experience to a job-ready Data Scientist. Master Machine Learning and Generative AI with a curriculum designed by an IITM alumnus with 8+ years of industry experience. Next batch starts August 2026 — be ready for the December hiring season.

Months · 35 Classes

Learning Phases

Real Projects

Coding Exp Needed

Why Sirius?

Our method bridges the gap between academic theory and industry reality.

💡

Business-First Learning

Every concept is introduced by the business problem it solves — no abstract theory without context.

🧠

The A-I-O-M-E Framework

Architecture → Intuition → Optimization → Math → Evaluation. Master any algorithm, step by step.

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From Zero to Deployed AI

Build an EDA mini-project, a predictive model, and a fully deployed Generative AI capstone you can show employers.

📈

Portfolio & Interview Prep

ATS-friendly resumes, LinkedIn optimisation, live-coding strategies, and STAR interview coaching.

🌐

End-to-End GenAI

From Transformers and LLM APIs to RAG systems, agentic workflows, and fine-tuning — you'll build it all.

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Weekend-Friendly Schedule

Each class is a 3-hour intensive session, designed for working professionals and students alike.

Who Can Join?

✔ Absolutely Zero Coding Experience Required

Whether you are a fresh college graduate looking to enter tech or a mid-career professional aiming to switch domains — all you need is a working laptop and the curiosity to learn.

The Placement Blueprint

35 live classes · 7 phases · 4 months · From scratch to job-ready

Phase 1: Python Foundations Classes 1-4 ▼

Class 1 Python Syntax & Logic. Variables, data types, if/else, basic loops.

Class 2 Data Structures. Lists, Dictionaries, Sets, Tuples. Nested loops.

Class 3 Functions & Error Handling. Reusable code, lambda, try/except.

Class 4 OOP Basics. Classes, objects, and a mini Python script from scratch.

Phase 2: Data Handling & Essential Math Classes 5-10 ▼

Class 5 Vectorization with NumPy. Arrays, matrices, mathematical operations.

Class 6 Data Wrangling with Pandas. Series, DataFrames, filtering, group-bys, missing values.

Class 7 EDA & Visualization. Spotting trends and outliers with Matplotlib & Seaborn.

Class 8 Feature Engineering. Encoding categoricals, scaling, creating features.

Class 9 Intuitive Math & Stats. Descriptive stats, probability, essential linear algebra/calculus.

Class 10 SQL for Data Extraction. SELECT, JOINs, and Window Functions.

Phase 3: Core Machine Learning Classes 11-21 ▼

Class 11 Linear Regression. y = mx + b at scale. Ridge & Lasso regularization.

Class 12 Logistic Regression. Classification basics and the sigmoid function.

Class 13 Decision Trees. Splitting criteria (Gini, Entropy) and feature importance.

Class 14 Random Forests. Ensemble learning, bagging, preventing overfitting.

Class 15 Gradient Boosting. XGBoost & LightGBM for tabular data.

Class 16 SVMs & KNN. Margin maximization and distance-based classification.

Class 17 Unsupervised Learning. K-Means and hierarchical clustering.

Class 18 Dimensionality Reduction. PCA & t-SNE for high-dimensional data.

Class 19 Model Evaluation. Precision, Recall, F1-Score, ROC-AUC, RMSE.

Class 20 Hyperparameter Tuning. Grid Search, Random Search, Cross-Validation.

Class 21 Mid-Term Project. End-to-end predictive model from scratch.

Phase 4: Deep Learning Foundations Classes 22-25 ▼

Class 22 Neural Network Basics. Perceptrons, activation functions, forward/backward propagation.

Class 23 PyTorch/TensorFlow Workflow. Building/training a Multilayer Perceptron.

Class 24 Computer Vision (CNNs). Convolutions, pooling, building an image classifier.

Class 25 Sequential Data (RNNs/LSTMs). Time-series and intro to early text modeling.

Phase 5: Generative AI & LLMs Classes 26-31 ▼

Class 26 NLP Foundations. Tokenization, embeddings (Word2Vec), traditional limits.

Class 27 The Transformer Architecture. Attention mechanism, BERT to GPT shift.

Class 28 Working with LLM APIs. OpenAI/Anthropic APIs, prompt engineering, open-source models.

Class 29 Retrieval-Augmented Generation (RAG). Vector databases, document chunking, querying.

Class 30 AI Orchestration Frameworks. Agentic workflows with LangChain / LlamaIndex.

Class 31 Fine-Tuning Basics. PEFT and LoRA without massive compute.

Phase 6: Deployment & Capstone Classes 32-33 ▼

Class 32 Model Deployment. Building APIs with FastAPI, creating UI with Streamlit.

Class 33 Capstone Presentations. Presenting final end-to-end GenAI / ML application.

Phase 7: Career Readiness Classes 34-35 ▼

Class 34 The Portfolio & Profile. ATS-friendly resumes, LinkedIn optimisation, GitHub portfolio.

Class 35 The Interview Prep. Take-home assignments, live-coding strategies, STAR interviews.

Know Before You Join

Everything you need to make an informed decision.

🏥 Who is this for?

Whether you're a fresh college graduate looking to break into tech or a mid-career professional aiming to pivot into Data Science — this course is built for you. With zero coding prerequisites and a weekend-friendly schedule, we meet you where you are and take you all the way to job-ready.

⚠️ The Hard Truth About Other Institutes

Most institutes teach AI/ML at surface level — they cover definitions, shallow concepts, and textbook examples. The result? Students can't answer even basic interview questions, let alone contribute in a real workplace. You end up with a certificate but zero confidence.

That changes at Sirius. We don't just teach — we build practitioners.

🎓 Why Sirius is Different

Curriculum by an IITM alumnus with 8+ years of hands-on industry experience across ML and Generative AI — not by academic theorists.
Deep-dive pedagogy. Every algorithm is taught through the A-I-O-M-E framework: Architecture, Intuition, Optimization, Math, and Evaluation. You'll understand the why behind every line of code.
Industry experts, not generic tutors. Since Sirius Institute is new, every batch gets direct attention from top-quality industry professionals who work with these technologies daily.
You won't be a number. Limited batch sizes mean personalised mentorship — not a recorded lecture with 500 strangers.

💪 What You'll Walk Away With

Real confidence to face technical interviews — live-coding, take-home assignments, and system design discussions.
Active placement support. Our team works with you on ATS-optimised resumes, LinkedIn profiling, GitHub portfolios, and mock interviews.
3 portfolio-ready projects including a fully deployed Generative AI application.
A clear career path into Data Science, ML Engineering, or AI roles.

📅 The Timeline — A Rare Opportunity

Sirius Institute is new, and that means our first batches get unprecedented access and attention from our founding team. This is a rare opportunity you won't find again once we scale.

Next batch: August 2026

Placement/hiring season: December 2026

Start your new career: 2027

This timing is no accident. The course is designed to align perfectly with the annual hiring surge, giving you maximum opportunity.