Al & Machine Learning
Course In Vellore
Fast‑track your career in AI & Machine Learning in 3–6 months. You’ll begin with Python fundamentals and data analytics in Jupyter, then move through practical ML (scikit‑learn), deep learning (TensorFlow & PyTorch), and advanced generative AI (GANs, VAEs, LLMs with OpenAI GPT & LangChain). You’ll learn to build and deploy production‑ready pipelines using FastAPI, Docker, and Kubernetes, culminating in capstone projects like an LLM chatbot, YOLO object detector, fraud‑detection API, recommender system, GenAI tool, and Edge AI for IoT.
Course Request Form
Course Duration
3 to 6 months
Python Essentials and Coding Bootcamp
- Python Setup, Data Types and Control Flow
- Functions, Modules and OОP
- Jupyter and Version Control
Data Science and Analytics Toolkit
- Data Manipulation with Pandas and NumPy
- Statistical Analysis, EDA and Hypothesis Testing
- Visualization with Matplotlib, Seaborn and Plotly
Practical Machine Learning Lab
- Supervised and Unsupervised Learning
- Ensemble Methods and Recommendation Engines
- Cross-Validation and Evaluation
Deep Learning Studio
- ANNs, CNNs, RNNs and Transformers
- Transfer Learning and Object Detection
- Hyperparameter Tuning and Optimization
Generative Al Primer
- GANs, VAEs and LLMs Fundamentals
- Prompt Engineering Basics
- Image/Audio/Text Generation
Applied Generative Al
- LLM Fine-Tuning and Zero-Shot Learning
- Multi-Modal GenAI Tools
- Integration with APls and Pipelines
Foundation Models and Al Platforms
- Use GPT, Llama, Claude and Gemini
- APl integration and RAG pipelines
- Deployment with FastAPI and MLflow
Learning Outcomes & Capstone Projects
- Master Python, ML, DL, and GenAl concepts
- Build production-ready Al pipelines
- Deploy models with FastAPI and Docker
- Integrate foundation models and LLMs
- Apply prompt engineering and RAG frameworks
- Handle MLOps, CI/CD, versioning and cloud deployment
- Capstone Projects: LLM chatbot, YOLO object detection, fraud detection APІ, recommender system, GenAl tool, Edge Al for loT
Industrial Visit
Explore innovation firsthand with Joy Innovations’ industrial visit. Interact with experts, witness cutting-edge technology, and gain insights in just one to two hours. Discover the future today!
*Duration One to Two Hours
Answers to Your Queries
Frequently Asked Questions
Who should enroll in this AI & Machine Learning Professional Program?
Anyone looking to transition into data science or AI engineering—whether you’re a Python developer wanting to specialize in ML, a data analyst aiming to learn deep learning, or a complete beginner ready to start with Python essentials.
What prior knowledge do I need before starting?
No ML background is required. You should be comfortable with basic computer use; we cover Python setup, core syntax, control flow, functions, OOP, Jupyter notebooks, and Git before moving into data science and AI.
Which ML and AI frameworks will I work with?
You’ll gain hands‑on experience with scikit‑learn for classic ML, TensorFlow & PyTorch for deep learning, and OpenAI GPT & LangChain for generative AI—along with tools like FastAPI for serving models, Docker/Kubernetes for containers, and CI/CD best practices.
How is generative AI incorporated into the curriculum?
We dive into GANs, VAEs, and transformer‑based LLMs, teach you prompt engineering fundamentals, and guide you through fine‑tuning and zero‑shot workflows—culminating in multi‑modal GenAI projects that generate text, images, or audio.
What real‑world capstone projects will I complete?
You’ll build production‑style applications such as an LLM‑powered chatbot, a YOLO object‑detection service, a fraud‑detection API, a recommendation engine, a custom GenAI tool, and even an edge‑AI solution for IoT devices.
How will I learn deployment and MLOps skills?
Through hands‑on labs you’ll containerize models with Docker, orchestrate them on Kubernetes, expose them via FastAPI, and implement versioning, monitoring, and CI/CD pipelines—so you can take AI solutions from notebook to production.
Looking for something else? Click here for our detailed FAQs page
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I like to learn many things in python it was easy to forget me now.
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experience I your institution.
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