AI Full Stack · UpsSkills

Learn AI. Build complete AI-powered applications.

Start with Python and machine learning, move into deep learning, LLMs, prompt engineering, embeddings and RAG, then connect AI models to Django APIs, React interfaces, vector databases and deployment workflows. UpAskAI is included as part of the learning experience.

Python Machine Learning LLM & RAG Django APIs Vector Databases AI Deployment

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AI product path

UpAskAI

Python & Data Foundation Python, NumPy, Pandas and practical data handling.

ML & Deep Learning Model training, evaluation, neural networks and AI fundamentals.

LLMs, Embeddings & RAG Prompting, retrieval, vector search and LLM application flow.

AI Backend APIs Django, DRF, authentication and AI service integration.

AI User Experience React, streaming responses, chat interfaces and frontend integration.

Deploy an AI Product Containers, model/API deployment and complete AI product practice.

The active batch can adjust topic order and tools based on the learning plan and available services.

AI Foundations Understand model concepts before moving into application integration.

Generative AI Learn how LLMs, embeddings, retrieval and RAG fit into AI applications.

Full Stack Integration Connect AI logic to APIs, databases and user-facing web interfaces.

Product Practice Apply the stack through labs, API work and end-to-end AI product development.

Course curriculum

AI Full Stack syllabus

The curriculum follows the core progression already present in your course: Python → ML → Deep Learning → LLM/RAG → Full Stack → Retrieval → MLOps → Capstone.

Python syntax, data types and OOP

NumPy and Pandas

Data analysis and visualization concepts

Practice: data analysis pipeline

Regression and classification

Clustering and unsupervised learning concepts

Scikit-learn workflow and model evaluation

Practice: prediction / recommendation workflow

Neural network fundamentals

PyTorch and TensorFlow foundations

CNN, RNN and transformer concepts

Apply a trained model through an application interface

Large language model fundamentals

Prompt engineering and context design

LLM API integration concepts

RAG and fine-tuning concepts

Practice: AI chatbot / assistant flow

Django views, models, ORM and authentication

Django REST Framework

API authentication and versioning concepts

Connect AI logic to application endpoints

Build an AI-powered REST backend

React components and hooks

State and server-data handling concepts

Streaming response UI patterns

Chat and AI interaction interfaces

Build an AI assistant frontend

PostgreSQL and Redis concepts

Embeddings and vector search

ChromaDB and vector database concepts

Retrieval-augmented generation pipeline

Practice: semantic search / RAG workflow

Packaging models and application services

Docker and container concepts

CI/CD and deployment workflow concepts

Cloud / hosted model deployment concepts

Deploy a model-backed API

Define the problem and product flow

Build data / retrieval / model components

Build backend APIs and frontend interaction

Test, debug and prepare deployment

Complete and demonstrate an AI-enabled product

Learning experience

Understand the AI stack as one system

The value is not only learning separate AI libraries. Students should understand how data, model logic, retrieval, APIs and the user interface connect inside an application.

Use UpAskAI while learning

Ask course-aware questions about Python, ML, neural networks, LLMs, RAG, vector databases and related AI topics available in UpAskAI.

Trace the complete AI flow

Learn how a user request can move through the frontend, backend, retrieval layer, model or LLM service, database and final response.

Build and explain the product

Apply concepts through labs and project work, then practise explaining the architecture, data flow, model choice and application behavior.

Technology stack

Technologies in the AI Full Stack path

Python

NumPy / Pandas

Scikit-learn

PyTorch / TensorFlow

LLM APIs

ChromaDB

Django / DRF

React

Docker

MLOps / Deployment

Course enquiry

Interested in AI Full Stack?

Contact UpsSkills for the current batch schedule, training format, fee and course-access details.

What is included

The active batch can vary in schedule and tool emphasis. The core course experience focuses on AI foundations, generative AI, application integration, projects and UpAskAI support.

Structured AI Full Stack curriculum

Machine learning and deep learning concepts

LLM, embeddings and RAG learning

Django / API / frontend integration

Project-oriented AI application practice

UpAskAI course-aware learning support

Questions

Before you join

The course path begins with Python and data-handling foundations, then moves into machine learning, deep learning, generative AI, full-stack integration and deployment.

UpAskAI is part of the UpsSkills learning experience and supports available AI, Python, machine learning and related course topics.

Yes. Use the Try UpAskAI option for the free learning trial, or contact UpsSkills on WhatsApp for an AI Full Stack course demo.

Please contact UpsSkills at 79959 14429 for current batch and fee information.

See how AI learning works inside UpsSkills.

Try UpAskAI first or speak with us about the AI Full Stack course. Understand the learning experience before deciding.

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