Advanced GenAI Development Course for All

Welcome to the most advanced and complete course in generative AI development. This course is perfect for students and people with no programming background. Our focus is on UI designing, UI development, web development, with generative AI tools & frameworks like Python, Hugging Face, LangChain, LLMs, vector databases, and AI agents.  

This course offers 100% placement support for anyone. 

Overview of Master Diploma in Generative AI development

ADMEC Multimedia is a pioneer institute in Delhi providing training since 2006 with 99% placement record. This advanced course is prepared for beginners and students willing to learn modern day tools and apps using generative AI. 

Our course covers all tools, frameworks, and techniques needed to be a successful Gen AI developer. This course starts from web development and ends with generative AI development. 

Who should join this diploma course

• Anyone looking for a career in generative AI development 
• Best course for web designers, graphic designers, and UI/ UX designers looking for an upgrade 
• Engineers, any professional, and sales & marketing people 

Prerequisites to join our GenAI Master program

Candidate must have good knowledge of these contents. 

• Familiarity with computer basics and internet 
• No prior AI or Gen AI development knowledge is needed 
• 10th or high school or equivalent diploma 

Know why  it is the best GenAI course for all?

This course covers generative AI with web development which offers great career. Its content makes it unique and career available course for you. You can go through below given course content to understand how well-planned and well-structured course is this. 

Come and learn generative AI today to align your skills with the industry. It will surely give a boost to your career. 

Top Skills to Acquire in this course

This course covers everything to prepare for GenAI developer profile. You’ll master top in-demand technologies covering Figma, HTML5, CSS3, JavaScript, ReactJS, Node.js, MongoDB, SQL, Python, Statistics, ML & DL Basics, NLP, Gen AI, App Development, Deployment, and many more. 

Syllabus of Advanced Diploma in Generative AI Development

This course is divided into 2 semesters each 6 months. Semester 1 focuses on UI designing, UI development, and server–side development with databases. 

Semester 1: GenAI Introduction, UI Designing, and Development

This semester is divided into 4 modules which focus on web related languages and tools to design a fully functional interface. You will learn UI designing, UI development, and server-side programming language like Node.js with database. 

Module 1: Gen AI Introduction and UI Designing 

This module emphasizes on introduction of Gen AI and UI designing using Figma. Before you start with development, an UI is needed. Whether you like to design or not, but the entire process and knowledge of related tools is essential for generative AI developers. 

Check out the below given content of this module. 

• Introduction to Gen AI 
• Gen AI course route map 
• Basics of user interface designing 
• Design of user interfaces using Figma 
• Micro-interaction and prototyping 
• Sharing Figma files for development 
• Project on UI designing 

Learning Outcomes of Module 1 

You will be master in designing interfaces for mobiles and websites using today’s leading tool i.e. Figma. Designing, micro-interaction, and prototyping are the key skills which you will master here. 

Module 2: UI Development 

When your user interface is ready, it is the time to start development. UI development means, conversion of a user interface to clickable and interactive pages with the help of programming. 

A list of a few important content is given below. 

• Introduction to UI development 
• Exploring UI languages 
• HTML5 & CSS3 
• Tailwind CSS 
• SASS – A CSS preprocessor 
• JavaScript with ES6 features 
• ReactJS with Redux 
• Next.js – A full stack framework for React 
• Project on UI development 

What Will You Learn in Module 2 

You will master UI development using all above leading languages from the experts of ADMEC Multimedia. You will be a versatile UI developer. It offers multiple technologies to develop an UI – HTML5 & CSS3, JavaScript, React, etc. 

Module 3: Server-side Programming and Databases

When your UI is ready, now it is the time to implement back-end or server-side logics. This module will make you learn database and Node.js to implement back-end logics. 

 List of the important topics: 

• Introduction to server-side programming 
• Exploring databases like MySQL and MongoDB 
• Diving deeper inside Node.js 
• Connecting Node.js with databases 
• Node.js with Express framework 
• Full project in Node.js 
• MERN stack development project 

What Skills will You Master in Module 3

After this module, you will be expert in Node.js and database with ExpressJS framework. You will be developing full-stack solutions with all the latest features like login system, user authentication, content management, comment management, user management, file system, etc. 

Now, you are able to create REST and SOAP APIs which will be used in React. 

Module 4: Code Optimization, Security, and Publishing

It is quite important module but mainly ignored by the institutes and students. We will focus here on performance and security of the projects. 

List of the important topics: 

• What is code optimization? 
• Benefits of code optimization 
• Optimizing all the code 
• Security and its importance 
• Implementing security 
• Preparing Node.js app for publishing 
• Platforms to publish a Node.js app 

Skills You Will Learn 

Code optimization, security, and publishing are the a few skills you will learn in this module. These skills are the super advanced skills and very useful in the development cycle. 

Semester 2: Data Analysis and Gen AI Development 

This semester is spread over 9 modules. It totally focuses on Gen AI development using Python, various databases, data analysis, prompt engineering, NLP, and various Gen AI tools. 

Module 1: Data Analysis Using Python

Learn all intricacies of data visualization using Python, SQL, and statistics in this module. You will be learning advanced Python with Django framework to develop high level websites in not time. Explore Streamlit in Python to develop initial level generative AI apps. 

 
Python 

• Introduction to Python 
• Control flow 
• Data structures 
• Functions and loops 
• Working with modules 
• File handling 
• Handling exceptions 
• OOPs in Python 
• Python for data science & analysis 
• Data visualization in Python 
• Streamlit with Python for Gen AI apps UI 

Statistics 

Part 1 – Math refresher 

• Useful concepts of math 
• Use of algebra, geometry, and calculus in data analysis 
• Data table, charts, graphs, etc. 

Part 2 – Statistics 

• Statistics in data analysis 
• Types of statistics 
• Types of data 
• Categorical data 
• Numerical data 
• Association between categorical & numerical variables 
• Correlation and covariance 
• Hypothesis testing 

Part 3 – Advanced statistical techniques 

• Multivariate regression 
• Classification problems 
• Predictive analytics 
• Text analytics 

Module 2: Prompt Engineering

Prompt engineering has become a very important skill these days to use AI tools at their fullest.  

Read below about the important topics you will learn. 

• What is prompt? 
• Use of prompt engineering 
• Prompt engineering in Gen AI 
• Types of prompts 
• Prompt designing tips 

Module 3: Explore NLP 

NLP is one of the core concepts of the generative AI development or engineering. Our focus is on in-depth NLP training here in this module. 

A few important topics are given below: 

• NLP – Exploring basics 
• Deep learning for NLP 
• Neural networks 
• Text Preprocessing – Tokenization, Lowercasing, Removing punctuation, Stopword removal, Stemming & Lemmatization, and POS (Part-of-Speech) tagging 
• Text Representation – Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), One-hot encoding, Word Embeddings (Word2Vec, GloVe, FastText), etc. 
• Regular Expressions – Pattern matching, Text cleaning and extraction 
• Named Entity Recognition (NER) – Recognizing entities like names, places, and dates, SpaCy, NLTK implementations 
• Text Classification – Sentiment analysis, Spam detection, Topic classification, Naive Bayes, Logistic Regression, SVMs for text 
• Language Modeling – N-gram models, Perplexity, Predictive text generation basics 
• Information Retrieval – Similarity and ranking, Cosine similarity, Jaccard similarity, and TF-IDF with search queries 
• Sequence Models – RNNs, LSTMs, GRUs, Sequence-to-sequence models, and Time series text generation 
• Transformer Models – Attention mechanism, Architecture of Encoder & Decoder, BERT, GPT, T5, XLNet, RoBERTa, Transfer learning in NLP, and Text Generation with Transformers 

Module 4: LangChain and Hugging Face

These 2 are the most essential tools for generative AI development. Where primary use of LangChain is for building AI apps using LLMs & tools, while Hugging Face is used for hosting, using, and training the ML models. 

In simpler words, LangChain is a framework for building structure apps using LLMs like chatbots, workflow tools, Q&A over custom data, etc.  

Hugging Face is the popular platform for accessing and training open-source machine learning models like transformers. This is best for text-generation, translation, summarization, fine-tuning models, hosting of models & datasets, deployment of models. 

Have a look on the list of topics: 

• LangChain introduction 
• LangChain for generative AI 
• LangChain and Open AI 
• Introduction to LCEL (LangChain Expression Language) 
• Building basic LLM app using LCEL 
• Introduction to Hugging Face 
• Hugging Face transformers library usage basics 
• Hugging Face and LangChain integration 
• Pdf Query RAG with LangChain and AstraDB 
• Multi-language code assistant in CodeLama 
• Deploying Gen AI apps in Streamlit and Hugging Face 

Module 5: RAG – Retrieval Augmented Generation and Vector DBs 

Retrieval-Augmented Generation (RAG) and Vector Databases (Vector DBs) are core pillars of modern GenAI app development. They are used when building chatbots or assistants that can answer questions based on custom data. 

Check the list of content: 

• Introduction to RAG 
• Why do we use RAG? 
• Example of use cases 
• How RAG works? 
• Key components of RAG 
• Popular tools & frameworks to build RAG 
• Vector databases and vector index 
• Why vector databases are so useful? 
• Most common vector indexing algorithms 
• Popular vector databases 
• Use cases of vector databases 
• Graph databases and Cypher Query Language with LangChain 
• RAG with GROQ API and LLama3 

Module 6: LangGraph with LangChain 

LangGraph is an extension of LangChain. We use it to build stateful, multi-agent, or multi-step generative AI applications. These apps use a graph-based execution model. It’s particularly useful for complex agent logic-based apps. 

List of a few topics covered in this module: 

• Introduction to LangGraph 
  • What is LangGraph? 
  • How it extends LangChain 
  • Use cases: stateful agents, multi-step LLM workflows 
• Key Concepts 
  • Graph nodes 
  • Edges, state 
  • Multi-agent systems 
  • Loops and branching 
• Setup & Installation 
• Building Your First LangGraph 
• Adding Conditional Logic 
• Stateful Conversations 
  • Maintaining chat history 
  • Using memory for LLM 
• Using Tools & Agents 
  • Integrating LangChain tools (e.g., calculator, search) 
  • Building tool-using agents inside LangGraph 
  • Example: Research agent (search → summarize → write) 
• Multi-Agent Collaboration 
• LangGraph + Vector DBs (Pinecone, FAISS) 
• LangGraph + RAG (Retrieval-Augmented Generation) 
• Debugging & Visualization 
• Deploying LangGraph Apps 
  • Running LangGraph with a frontend (Streamlit, Flask, or React) 
  • API deployment 
  • Security and rate-limiting basics 
• Building Stateful and Multi-Actor Apps using LangGraph 

Module 7: LLM Models and their Fine Tuning

LLM (Large Language Model) is a model of artificial intelligence. These models are trained on massive amounts of text data to understand, generate, and reason using human language. 

Fine-tuning is important technique in generative AI development. It is a process of retraining a pre-trained LLM on a smaller and domain-specific dataset. 

List of the topics covered: 

• Introduction to LLM Models 
• Fine Tuning LLM Models 
• Types of Fine Tuning – Full training, adapter-based, and LoRA 
• Lamin Platform for End-to-End Fine Tuning 
• Overview of Models -Ollama, OpenAI, and xAI 
• Working with Open AI and Ollama 

Module 8: Gen AI Projects and Interview

You will be guided and trained on multiple Gen AI projects. These projects will guarantee a hardcore experience and give you wonderful projects for the interview. 

List of generative AI projects you will be working on: 

• YouTube Video Content Summarization 
• Search Engine with LangChain Tools and Agents 
• Gen AI with AWS (Amazon Web Services) 
• Nvidia NIM and LangChain 
• Developing Multi AI Agents with CrewAI 
• Search RAG with Vector DB and LangChain 
• Interview Preparation 

Module 9: No-code Workflow Automation Platforms 

We don’t want to leave anything uncovered in our course. These days no code or low code platforms are very popular in many sectors to build AI tools and workflow.  

This module covers below given no-code workflow automation platforms. 

• n8n 
• make.com 
• zapier 
• autocode 
• pipedream 

Career options after GenAI development training in Delhi 

Many career opportunities are opened-up for you, as you complete this advanced Gen AI development course. 

Explore the below given career options: 

• Gen AI developer 
• AI prompt engineer 
• AI product developer 
• LLP application manager 
• AI integration specialist 
• NLP engineer 
• Fine tuning specialist 
• UI designer 
• UI developer 
• React expert 
• Node.js developer 
• Web developer, etc.