Predictive Customer Churn Pipeline
An end-to-end machine learning system analyzing historical transaction logs, engineering behavioral features, training gradient-boosted classifier models, and setting up automated scheduled reports.
DSGAJob-Oriented Program
Data Science with Generative AI Master Program Course
Transform into a next-generation analytical engineer. Learn to design predictive machine learning architectures, engineer scalable Generative AI workflows, and deploy enterprise-level AI agent solutions.
Total Duration
12 Months
Track
6-8 Months
Learning Style
Practical + Enterprise Case Driven
Machine Learning Pipelines
Build and validate commercial predictive algorithms
Generative AI Systems
Master LLM fine-tuning, RAG frameworks, and agents
Data Infrastructure
Write robust database scripts and feature pipelines
Program Key Highlights
Why this 12-month master track feels clearer and easier to commit to
We bypass surface-level code snippets to focus entirely on industrial-scale system design and deep learning mechanics. Every student goes through intensive diagnostic programming labs, pipeline performance reviews, and multi-stage capstone defenses designed to match elite tech corporate standards.
360+
Hours of guided practical learning
24+
Structured production checkpoints
1:1
Personalized technical portfolio reviews
Capstones
Four complex deployed project outputs
96+
Interactive sessions
2400+
Student support touchpoints
Placement
Dedicated corporate drive access
Why Aspiring AI Engineers Choose This Master Track
A master-level analytical roadmap built for modern technical infrastructure
Comprehensive coverage of advanced Python development, statistics, and high-performance SQL queries.
Rigorous training in machine learning modeling, feature engineering, and validation strategies.
Deep-dive development of large language model architectures, vector databases, and semantic search.
Structured application of AI agent networks, custom workflow orchestration, and scalable cloud APIs.
12 Months
Comprehensive Track
4 Capstones
Real practice outputs
Strategic
Executive-level guidance
About This Course
A comprehensive multi-phase journey designed for complete technical domain authority
Modern businesses rely on advanced automated decision engines, semantic search intelligence, and predictive pipelines to operate at scale. The Data Science with Generative AI Master Program structures this massive, high-demand industry into a highly organized 12-month learning track. You start by mastering foundational mathematical logic, Python programming, and relational database systems, moving seamlessly into complex machine learning architectures, deep neural networks, and modern vector storage frameworks.
Guided systematically by active AI researchers and data science leads, you will learn the precise workflows required to design scalable systems—including fine-tuning open-source Large Language Models (LLMs), building Retrieval-Augmented Generation (RAG) platforms, and orchestrating multi-agent networks. By graduation, you will own an enterprise-grade GitHub portfolio proving your ability to deploy robust predictive models and cognitive AI services on public cloud infrastructures.
Eligibility
Who can confidently start this path
Graduates or final-year students (BCA, MCA, B.Sc, B.Tech, Mathematics) seeking an elite, industry-aligned career path in artificial intelligence, machine learning, and data engineering.
Software developers, database administrators, and traditional data analysts wanting to update their capabilities with deep learning and generative model integrations.
Aptitude-driven professionals wanting a complete, structured transition into cloud infrastructure automation, data engineering, and predictive systems development.
Course Curriculum
Learn through a structured roadmap, not through disconnected chapters
Each module represents a practical phase in the learning journey.
Module 1
Module 1: Mathematical Foundations & Advanced Python
+−
Module 1
Module 1: Mathematical Foundations & Advanced Python
- Linear Algebra, Calculus, and Probability Theory for Machine Learning
- Python Syntax Essentials, Asynchronous Scripts, and Data Structures
- Data Wrangling and Aggregation utilizing Pandas and NumPy Libraries
- Relational Databases: Complex Joins, Subqueries, and Windows Functions in SQL
Module 2
Module 2: Machine Learning & Feature Engineering
+−
Module 2
Module 2: Machine Learning & Feature Engineering
- Exploratory Data Analysis (EDA) and Advanced Statistical Hypothesis Testing
- Supervised Learning: Linear/Logistic Regression, Decision Trees, and Random Forests
- Unsupervised Algorithms: K-Means Clustering and Dimensionality Reduction (PCA)
- Model Validation: Cross-Validation, Precision-Recall, ROC-AUC, and Hyperparameter Tuning
Module 3
Module 3: Deep Learning & Natural Language Processing (NLP)
+−
Module 3
Module 3: Deep Learning & Natural Language Processing (NLP)
- Neural Network Foundations, Activation Functions, and Backpropagation
- Computer Vision Basics with Convolutional Neural Networks (CNNs)
- Text Processing Foundations, Tokenization, and Word Embedding Vectors
- Recurrent Neural Networks (RNNs) and the Shift to Attention Mechanisms
Module 4
Module 4: Generative AI Architecture & Large Language Models (LLMs)
+−
Module 4
Module 4: Generative AI Architecture & Large Language Models (LLMs)
- Understanding Transformer Architectures, Encoder-Decoder Systems, and Self-Attention
- Working with Open-Source LLMs: Llama, Mistral, and API Integrations
- Prompt Engineering Systems: Chain-of-Thought, Few-Shot, and Structural Outputs
- Model Fine-Tuning Methodologies: Parameter-Efficient Fine-Tuning (PEFT) and LoRA
Module 5
Module 5: Retrieval-Augmented Generation (RAG) & Vector Storage
+−
Module 5
Module 5: Retrieval-Augmented Generation (RAG) & Vector Storage
- Understanding RAG Pipelines: Document Chunking, Embeddings, and Indexing
- Implementing Vector Databases: Pinecone, ChromaDB, and Milvus Configurations
- Semantic Search Optimizations: Hybrid Retrieval and Reranking Systems
- Handling Context Constraints, Query Rewriting, and Response Valuations
Module 6
Module 6: Cognitive AI Agents & Cloud Operations
+−
Module 6
Module 6: Cognitive AI Agents & Cloud Operations
- Designing AI Agent Networks with LangChain, CrewAI, and Autogen
- Configuring Tool Calling Capabilities, System Memory Layers, and Iterative Loops
- Packaging Machine Learning Models as Production APIs using FastAPI
- Containerizing AI Services via Docker and Deploying onto Cloud Instances (AWS/GCP)
Build Industry-Based Projects
What kind of practical output learners can expect from this course
Enterprise RAG-Powered Knowledge Base System
A production-ready Retrieval-Augmented Generation platform built with dynamic PDF parsing layers, semantic vector indexing inside ChromaDB, secure LLM integrations, and custom reranking protocols.
Multi-Agent Corporate Analytics Assistant
A cognitive AI agent team constructed using LangChain and FastAPI to extract relational data via automated SQL queries, process statistical insights, and generate visual management briefings.
Tools They Will Learn
Category-wise tools, platforms, and software exposure
Data Pipeline & ML
Python (Pandas / NumPy)
PostgreSQL Server
Scikit-Learn
Generative AI Systems
LangChain
AWS EC2 Cloud
Certificate
Earn a master completion certificate you can proudly showcase
Secure an advanced AI engineering validation from StackCode IT. Demonstrate complete operational authority over statistical data processing, automated predictive models, and generative LLM pipelines directly to engineering leads.
Master Tier Validation
A verified professional certification designed to add significant technical weight to your resumes and portfolio headers. --- Pipeline Code Audited Granted strictly following the manual review, performance validation, and code architecture check of your deployed capstone projects. --- Enterprise Ready Signals to modern software engineering teams that you understand vector spaces, model scalability, data pipeline security, and cloud deployment.
Certificate Type
Training Completion
Track
Job-Oriented Program
Duration
12 Months
Job Scope After Course Completion
Where learners can start building their career after this course
AI Engineer
Data Scientist
Machine Learning Engineer
NLP Developer
Companies Hiring For These Skills
Roles from product brands and major service companies both value these skills
Enterprise technology product conglomerates, multinational financial consultancies, scaling analytics software groups, and IT consulting networks operating inside major Indian technology hubs like Ahmedabad, Gandhinagar GIFT City, and Surat actively onboard specialists capable of developing AI-driven architectures.
Testimonial Videos
Hear from our students
Explore More Reviews
Sc
Devansh Mehta
AI Engineer at CognitiveTech
The structural approach to RAG pipelines and custom vector database operations changed how I build software. Defending my agent project mock rounds gave me the perfect confidence to pass senior technical panels.
Sc
Nisha Raval
Machine Learning Specialist
I transitioned from standard business intelligence tools. The course handled math logic and coding progression extremely cleanly, allowing me to build deep learning systems from scratch.
Centres In Gujarat
Ahmedabad & Rajkot centers with guidance support
Ahmedabad
Ahmedabad Learning Center
A-1114 Siddhi Vinayak Tower, Kataria Automobiles Rd, Makarba, Ahmedabad, Gujarat 380051
Rajkot
Rajkot Learning Center
B-925 RK Iconic Tower, 150 Feet Ring Rd, nr. Shital Park, Rajkot, Gujarat 360006
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Frequently Asked Questions
Got questions? We've kept the answers simple, practical, and easier to scan before joining.
Is an extensive coding background mandatory before starting this master program?+−
No prior advanced software engineering experience is assumed. We start the technical track with foundational Python and relational SQL querying before progressing to complex predictive modeling, neural networks, and LLM integrations.