Master the Stack from Foundations
to Production-Grade Data Science & Gen AI
Build real-world ML engines, scalable RAG pipelines, and automated multi-agent systems. Break out of pure theory into full-stack engineering.
Join 5,000+ engineering professionals globally
Our Alumni Work At Top Enterprise Ecosystems
10-Week Structured Path
60-80 hours of structured instructor-led mechanics with an explicit hybrid tracking model.
Dual Production Capstones
Ship one full Machine Learning application and one production-shaped GenAI application end-to-end.
Enterprise Lab Tooling
Deploy on industry architectures like Pinecone, LangChain, and advanced Oracle 23ai vector engines.
Responsible AI Guardrails
Mitigate hallucination, prompt injection, and jailbreaking via structural moderation blocks.
The Weekly Execution Blueprint
6-8 hours per week engineered meticulously to drive absolute conceptual retention and deployment mastery.
Designed for Radical Career Acceleration
Students & Freshers
Establish a bulletproof operational base by deploying native matrix functions and building multi-layer models right from scratch.
Developers & Engineers
Transition smoothly from standard deterministic scripting into robust probabilistic high-dimension modeling with enterprise LLM framework integrations.
Analysts & Professionals
Deconstruct data pipelines systematically and interface directly with LLM architectures to extract and structure hidden document data nodes.
Explore Our 10-Week Syllabus Map
Comprehensive curriculum covering data manipulation, full ML lifecycles, and advanced multi-agent orchestrations.
MODULE 1: Foundations of AI, ML, DL & Data Science
Weeks 1-2 • Solid Conceptual Base & Your First Neural Network
MODULE 1: Foundations of AI, ML, DL & Data Science
Weeks 1-2 • Solid Conceptual Base & Your First Neural Network
Week 1: AI/ML/DS Foundations
- Intro to AI, ML & DS: History & evolution, narrow vs. general AI, real-world applications (chatbots, fraud detection, recommendations, resume screening), AI vs. traditional programming.
- Math Foundations: Linear algebra (vectors, matrices, dot product), calculus essentials (derivatives, gradients, chain rule), probability & statistics (distributions, Bayes' theorem, expectation), hands-on with NumPy.
- Python Tools & Data Handling: Refresher on structures, NumPy, Pandas, Matplotlib, Seaborn. Jupyter/Colab setups, virtual environments. Reading CSV/JSON/Excel, data preprocessing, handling missing values/outliers, and EDA intro.
- ML Workflow Overview: Supervised vs. Unsupervised vs. Reinforcement learning, train-test splits, features vs. labels, model training intuition, and evaluation metrics intro.
Week 2: Deep Learning Fundamentals
- Core Concepts: What is a neural network, biological vs. artificial neurons, the Perceptron model, and activation functions (ReLU, Sigmoid, Tanh, Softmax).
- Network Architectures: Input, hidden, and output layers. Forward propagation, loss functions (MSE, Cross-Entropy), backpropagation intuition, and gradient descent variants (SGD, Adam, RMSprop).
- Optimization & Tooling: Epochs, batch sizes, learning rates. Overfitting/underfitting management. Regularization (L1, L2, Dropout), batch normalization, and early stopping. TensorFlow vs. PyTorch framework overview & Keras API.
- Hands-on Lab: Build a feed-forward NN on MNIST / Iris, visualize training curves, tune hyperparameters, and compare model performance.
• Conceptual Quiz: AI/ML/DL mechanics (30 questions)
• Lab Notebook: First functional neural network on MNIST dataset
• Mini-Project: Exploratory Data Analysis (EDA) on a real-world dataset (e.g., Titanic, Housing prices)
MODULE 2: Deep Dive into Data Science & Machine Learning
Weeks 3-6 • Full ML Lifecycle from Data Wrangling to Model Deployment
MODULE 2: Deep Dive into Data Science & Machine Learning
Weeks 3-6 • Full ML Lifecycle from Data Wrangling to Model Deployment
Weeks 3-4: Engineering & Core Supervised Models
- Advanced EDA & Feature Engineering: Feature scaling (Standardization/Normalization), categorical encoding (One-Hot, Label, Target), feature selection techniques. Hypothesis testing, confidence intervals, p-values, and statistical significance.
- Supervised Learning Foundations: Linear, Multiple, and Polynomial Regression. Ridge, Lasso, ElasticNet architectures. Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, and Decision Trees.
- Validation & Metrics: Stratified k-fold cross-validation, bias-variance tradeoff. Hyperparameter tuning (GridSearch/RandomSearch). Evaluation metrics mapping: Precision, Recall, F1-Score, ROC-AUC matrices. Handling imbalances via SMOTE synthesis.
Weeks 5-6: Ensembles, Deep Architectures & MLOps
- Advanced ML Frameworks: Bagging and Boosting mechanics (Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost). Unsupervised layouts (K-Means, Hierarchical clustering, DBSCAN, PCA mapping). Time Series modeling (ARIMA, Prophet). Recommender Systems.
- Neural Sequences & Operations: Convolutional Neural Networks (CNN) for spatial parsing, Recurrent Neural Networks (RNN/LSTM/GRU) for contextual time series and texts. Introductory transfer learning protocols.
- Production Deployment: Model serialization paths (Joblib, ONNX). Constructing lightweight microservices via FastAPI. Packaging environments through Docker container nodes. MLflow versioning configurations and drift tracking loops.
• 4 Production-grade Graded Jupyter Notebook Coding Assignments
• Mid-Module Comprehensive Validation Exam (Supervised Learning Metrics)
• Module 2 Capstone Delivery: End-to-end engineered predictive model served over containerized web APIs
MODULE 3: Generative AI & LLM Application Development
Weeks 7-10 • Master Transformer Foundations, RAG Pipelines & Autonomous Agents
MODULE 3: Generative AI & LLM Application Development
Weeks 7-10 • Master Transformer Foundations, RAG Pipelines & Autonomous Agents
Weeks 7-8: Attention Mechanism & API Interfacing
- Transformer Architectures: Discriminative vs. Generative logic. Self-attention, multi-head attention blocks, positional encoding matrices. Complete structural breakdown of the "Attention Is All You Need" paper walkthrough. Parameters, scaling laws, and BERT/GPT family deep dive.
- Advanced Prompt Architectures: Programmatic zero-shot, few-shot prompting layouts. Chain-of-Thought (CoT) pathways, Tree-of-Thoughts, and ReAct frameworks. Sampling mask control variables (Temperature, Top-K, Top-P).
- API Runtimes & Content Safety: Interfacing with OpenAI, Anthropic Claude, Google Gemini APIs. Response streaming logic, token economics, and latency reduction blocks. Building custom safety guardrails to mitigate prompt injections, jailbreaks, and PII leaks.
Weeks 9-10: RAG Orchestrations, Fine-Tuning & Agents
- Retrieval-Augmented Generation (RAG): Spatial vector indexing engines (Pinecone, ChromaDB, FAISS, Oracle DB 23ai Vector Search). Complex text chunking layouts, similarity tracking functions (Cosine, Dot product). Citation mapping structures.
- Framework Primitives & Fine-Tuning: Orchestrating complex orchestration paths over LangChain and LlamaIndex templates. Parameter-Efficient Fine-Tuning (PEFT) frameworks using SFT, LoRA adaptations, and quantized QLoRA layouts over Hugging Face nodes.
- Autonomous Multi-Agent Systems & Multimodal App Dev: Building function calling nodes, autonomous tool-tracking loops, and Model Context Protocol (MCP) integrations. Prototyping UIs with Streamlit/Gradio and Next.js. Observability frameworks (RAGAS, LLM-as-a-judge telemetry, tracing loops).
• Functional conversational assistant engine tracking core streaming API endpoints
• Production-grade context-grounded RAG application querying local enterprise indexes
• Fine-tuning experiment notebook tracking LoRA parameter adjustments over custom data targets
• Module 3 Capstone Delivery: Full-stack context-aware GenAI application prototype and deployment dossier
The Modern Enterprise Intelligence Stack
Gain deep programmatic proficiency across the exact libraries and APIs shaping production environments today.
Practical Capstone Modules
Deconstruct and deploy high-utility architectures instead of basic template scripts.
End-to-End Enterprise Predictive Engine
Collect data fields, structure custom engineering configurations, train optimized boosting parameters, serialize model versions, and host code architectures via dynamic FastAPI nodes.
Full-Stack Context-Grounded Generative Application
Construct a decoupled responsive interface linking directly to high-dimension Vector Indexes. Enable custom parsing mechanics, automated prompt loops, and structured validation layers.
Targeted Market Engineering Roles
Transition fully from simple descriptive analytics into native development parameters.
Why Engineers Choose ExaGuru?
Zero Trivial Demos
We do not copy standard boilerplate scripts. You build operational, memory-backed workflows designed for robust high-dimension enterprise search execution.
Syllabus Depth Rigor
Meticulous alignment starting right from fundamental regression assumptions up to parameter-efficient fine-tuning matrices.
Robust Assessment Verification
Iterative conceptual checks, graded lab files, and rigorous peer code evaluations designed to confirm system design capabilities.
Program Certification Evaluation
What Our Engineers Achieve
"The structural mapping of transformer networks finally clicked during Week 7. Building full RAG pipelines inside custom vector nodes completely decoupled my dependency on basic cloud scripts."
- Systems Developer"Moving sequentially from simple linear gradients to supervised fine-tuning modules gave me a solid, math-grounded architectural portfolio that standard bootcamps fail to deliver."
- Infrastructure Lead"The dual-capstone review parameters forced absolute code optimization. Having my logic tracked, benchmarked, and containerized completely elevated my engineering capabilities."
- Data EngineerFrequently Asked Mechanics
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Secure Your Cohort Matrix Seat
Select your alignment paradigm below. EMI accommodations available at terminal checkouts.
- Complete 10-Week Core Master Portal Access
- Downloadable Notebook Guides & Code Scaffolding
- Lifetime Alumni Developer Node Membership
- Evaluation Infrastructure Validation Loops
- Complete 10-Week Core Master Portal Access
- Downloadable Notebook Guides & Code Scaffolding
- Lifetime Alumni Developer Node Membership
- Evaluation Infrastructure Validation Loops
96-HOURS, 100% RISK- FREE ASSURANCE
Test the integration paths, deconstruct the material, and trace the code nodes. If the technical granularity fails to meet your baseline standards, acquire a full structural refund under the following bounds:
- Portal core material consumption status matches strictly less than 25%
- Formal module testing/assessment items remain completely unattempted
- Cancellation execution request is generated formally within 96 runtime hours of checkout