Generative AI with Python Course in Hyderabad
Build generative AI applications in Python with LLMs, embeddings, RAG and image models
The Generative AI with Python course teaches you to build practical generative AI applications using Python from the ground up. You begin with Python, APIs and data handling, then study how large language models work and call hosted models through their APIs with prompts, structured output and tool calling. You use Hugging Face Transformers to run open models, create embeddings and semantic search, build retrieval-augmented generation applications, and add memory and simple agents. You also explore text-to-image generation with diffusion models, speech and vision models at an overview level, and parameter-efficient fine-tuning with LoRA. Applications are built with Streamlit or Gradio and served with FastAPI, with evaluation, safety, cost control and deployment with Docker. The course suits graduates, developers and data professionals; basic programming helps and Python is taught from the basics. It is tool-neutral and uses open-source libraries alongside hosted APIs. You build several apps and a capstone. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Python for AI Development
- •Python syntax, data types, functions and classes
- •Virtual environments, pip and project structure
- •Reading JSON and working with files and APIs
- •Lab: Build a small Python tool that calls a public API
- •Outcome: Write clean Python for AI projects
Data Handling with NumPy & pandas
- •NumPy arrays and vectorised operations
- •pandas DataFrames, grouping and cleaning
- •Plotting with Matplotlib
- •Lab: Explore and clean a text dataset
- •Outcome: Prepare data for AI workflows
Generative AI & LLM Fundamentals
- •What generative AI is: text, image, audio and code models
- •Transformer and attention ideas at an intuitive level
- •Tokens, context windows, temperature and cost
- •Lab: Compare models and settings on the same prompt
- •Outcome: Explain how LLMs generate text and where they fail
Working with LLM APIs
- •Calling hosted chat model APIs with Python
- •Streaming, structured output and function calling
- •Rate limits, errors, retries and cost tracking
- •Lab: Build a command-line assistant with tools
- •Outcome: Build reliable LLM-powered features
Prompt Engineering for Developers
- •Prompt patterns and templates in code
- •Prompt versioning and small evaluation sets
- •Defending against prompt injection basics
- •Lab: Compare prompt versions with a test set
- •Outcome: Treat prompts as testable code
Hugging Face Transformers & Open Models
- •Hugging Face pipelines, tokenizers and the model hub
- •Running open models locally with Transformers or Ollama
- •Quantisation and hardware considerations
- •Lab: Run a small open model and compare it with a hosted model
- •Outcome: Choose between hosted and open models
Embeddings & Semantic Search
- •Embeddings and similarity metrics
- •Vector stores such as FAISS, Chroma and pgvector
- •Building semantic search with metadata filters
- •Lab: Build a semantic search over a document set
- •Outcome: Search by meaning, not keywords
Retrieval-Augmented Generation
- •RAG architecture; document loading and chunking
- •Retrieval, re-ranking and cited answers
- •Evaluating answer quality
- •Lab: Build a PDF question-answering app
- •Outcome: Answer questions from private documents
Agents, Memory & Workflows
- •Agent loop, tools and memory
- •Using LangChain and LangGraph at an introductory level
- •Human approval and failure handling
- •Lab: Build an agent that uses search and calculation tools
- •Outcome: Build simple agents that do multi-step tasks
Image & Multimodal Generation
- •How diffusion models generate images
- •Hugging Face Diffusers, prompts, seeds and guidance
- •Vision-language models and speech models overview
- •Lab: Generate and edit images with a diffusion pipeline
- •Outcome: Use image and multimodal models responsibly
Fine-Tuning with LoRA
- •When to fine-tune and when not to
- •Dataset preparation; PEFT and LoRA with Transformers
- •Evaluating fine-tuned models and sharing adapters
- •Lab: Fine-tune a small model with LoRA on a toy dataset
- •Outcome: Adapt a model to a narrow task
Apps, APIs & Deployment
- •Building interfaces with Streamlit or Gradio
- •Serving with FastAPI and packaging with Docker
- •Caching, logging, safety filters and cost control
- •Lab: Deploy a RAG app as an API and a web UI
- •Outcome: Ship generative AI apps
Responsible AI & Capstone
- •Bias, privacy, copyright and misuse considerations
- •Evaluation and monitoring in production
- •Capstone: end-to-end generative AI application with documentation and a demo
- •Portfolio write-up and interview practice
- •Outcome: A deployed, documented generative AI project
Tools Covered
Projects Included
- Text Assistant with Tools: Build an LLM assistant that calls calculation and search tools; deliverable is a command-line or web app and test prompts.
- Semantic Search App: Embed a document set and search by meaning with filters; deliverable is a Streamlit app and relevance examples.
- PDF Question Answering: Build a RAG app with citations and refusal on unknown answers; deliverable is a demo and evaluation sheet.
- Image Generation Studio: Create a prompt-driven image generator with seeds and guidance controls; deliverable is a Gradio app.
- Capstone Generative AI App: Combine retrieval, an agent and a deployed API for a chosen domain; deliverable is a GitHub repository, architecture diagram and demo.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Generative AI with Python course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Generative AI with Python.
Certificate of Completion
Generative AI with Python
This certifies that
[Your Name]
has successfully completed the
Generative AI with Python
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Generative AI with Python course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Generative AI with Python Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Generative AI Developer, AI Engineer, Python Developer (AI), NLP Engineer; support is guidance with the job search, not a guarantee of a job.