Y YATVIKIT
Batches Enrolling Now · Online

From Python Foundations
to Production-Grade AI Systems

A 6-month, project-first Data Science, Generative AI & Agentic Systems program — built for B.Tech students and fresh graduates who want to ship real AI systems, not just study theory.

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9CURRICULUM PHASES
6MONTHS, FULL PROGRAM
20+HANDS-ON PROJECTS*
yatvikit — curriculum.sh
✓ NO PRIOR AI EXPERIENCE REQUIRED ✓ PYTHON → ML → DL → LLMs → AGENTS ✓ GITHUB PORTFOLIO + CAPSTONE
Our Programs

Choose the path that fits where you're starting from

Every program is project-based and eligibility-mapped to your year of study — from a foundational 2-month track to a full 6-month AI engineering journey.

Flagship Program Duration · 6 Months

Data Science with GenAI & Agentic Systems

The complete journey: Python, data analysis, ML, deep learning, NLP/LLMs, generative AI and autonomous agents, MLOps, and an industry-grade capstone.

Mode: Online Eligibility: B.Tech 2nd year → fresh graduates
Python → ML → DL LLMs & RAG LangChain / LangGraph MLOps & Docker Capstone Project
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Duration · 4 Months

Generative AI & Agentic Systems

For those who've completed Python foundations — go straight into ML, deep learning, LLMs, generative AI, LangChain/LangGraph agents, and MLOps deployment.

Mode: Online Eligibility: B.Tech 4th year → fresh graduates
Deep Learning NLP & Transformers Generative AI Autonomous Agents
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Duration · 2 Months

Python for Data Science & AIML

A focused foundation track: Python programming, data structures, data wrangling & visualization, SQL/MongoDB, Power BI, and the math behind ML.

Mode: Online Eligibility: Open to all interested learners
Python & DSA NumPy & Pandas SQL & MongoDB Power BI
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Flagship Curriculum · 9 Phases

Every phase ends with something you built, not just a topic you covered

The full roadmap of the 6-month Data Science with GenAI & Agentic Systems program, phase by phase.

PHASE01

Python Programming Foundations

Core Syntax · OOP · DSA · File Handling
Syntax & control flow Lists, tuples, sets, dicts Searching & sorting Recursion, decorators, generators OOP & exception handling
You'll build: A command-line task manager built with OOP and file I/O, plus a pip-installable Python package.
PHASE02

Data Analysis & Visualization

NumPy · Pandas · SQL · MongoDB · Power BI
NumPy & vectorized ops Pandas wrangling & feature engineering Matplotlib / Seaborn / Plotly SQL joins & window functions Power BI + DAX
You'll build: An end-to-end EDA notebook, an interactive Plotly dashboard, and a Power BI report with DAX measures.
PHASE03

Mathematics for Data Science

Probability · Inference · Linear Algebra · Calculus
Probability & Bayes' theorem Distributions & CLT Hypothesis testing, A/B testing Vectors, matrices, eigenvalues Gradient descent intuition
You'll build: The statistical and mathematical grounding to build ML & DL models from first principles.
PHASE04

Machine Learning — Core Algorithms

Scikit-learn · Regression · Trees · SVM · Clustering
Regression & regularization Random Forest, XGBoost, SVM K-Means, DBSCAN, PCA Cross-validation & tuning Imbalanced data (SMOTE)
You'll build: A customer segmentation system with K-Means + PCA, and a fraud anomaly detector using DBSCAN & Isolation Forest.
PHASE05

Deep Learning & Computer Vision

PyTorch · TensorFlow · CNNs · YOLO
Neural network foundations Backprop, dropout, batch norm CNNs & image augmentation Transfer learning (ResNet, EfficientNet) Object detection with YOLO
You'll build: A custom CNN classifier, a transfer-learning pipeline, and a real-time YOLO object detection system.
PHASE06

NLP, Transformers & Large Language Models

BERT · GPT · HuggingFace · RAG · Fine-tuning
Tokenization, TF-IDF, embeddings RNN / LSTM / attention Transformer Prompt engineering, LoRA/QLoRA RAG & vector search
You'll build: A RAG prototype: ingestion → embedding → retrieval → grounded answers.
PHASE07

Generative AI & Agentic Systems

LangChain · LangGraph · CrewAI · MCP
Vector DBs (Pinecone, FAISS, Chroma) LangChain chains & LangGraph agents Multi-agent orchestration, CrewAI Human-in-the-loop, MCP & Agentic RAG
You'll build: A production RAG system, an autonomous research agent, a CrewAI content pipeline, and a custom MCP server.
PHASE08

MLOps & Model Deployment

FastAPI · Docker · GitHub Actions · CI/CD
Git workflows & model versioning FastAPI endpoint design Dockerizing ML apps GitHub Actions CI/CD Cloud deployment strategies
You'll build: A JWT-secured FastAPI model service, Dockerized with Compose, deployed through an automated CI/CD pipeline.
PHASE09

Capstone Project — Industry Grade

Data → Model → API → Dashboard → Cloud → Portfolio
Real-world problem selection End-to-end pipeline build GenAI or agentic component Resume & GitHub portfolio Mock interviews
You'll build: A fully deployed, job-winning AI portfolio project — reviewed and refined through mock interviews.
Skills Gained

What you'll be able to put on your resume

Python Programming Data Structures & Algorithms OOP NumPy & Pandas Data Visualization SQL & MySQL MongoDB Power BI Machine Learning Deep Learning (PyTorch/TF) Computer Vision & CNNs NLP & Text Processing Transformers & LLMs Prompt Engineering RAG Pipelines Generative AI (GANs/Diffusion) LangChain & LangGraph Autonomous AI Agents Vector Databases FastAPI & Docker MLOps & CI/CD Cloud Deployment CrewAI Orchestration
Tools & Frameworks Covered

The same stack production AI teams use

Python
NumPy
Pandas
Scikit-learn
TensorFlow
Keras
PyTorch
Matplotlib
Seaborn
MySQL
MongoDB
Power BI
Looker
FastAPI
Docker
GitHub
LangChain
LangGraph
CrewAI
HuggingFace
OpenAI
Gemini
Chroma
FAISS
Tavily
SQLAlchemy
VS Code
Ultralytics

Seats are filling for the next batch.

No prior AI experience required — just logical thinking and a willingness to learn.

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