Generative AI Development Course for Web & Software Developers

This course is perfect for web and software developers who want to build and integrate applications using Generative AI technologies like LLMs, vector databases, and AI agents. This course will be a great upgrade and lifesaver for developers facing threats from AI. 

Overview of GenAI development course

This course is a wonderful training module launched by pioneer institute ADMEC Multimedia in Delhi. IT professionals struggling to find their space in the industry can join this course to boost their value. 

This course covers all the essentials tools and techniques needed to be a gen AI developer today. We will start this course from scratch and cover everything till deployment. 

Who can join this course

• Intermediate to advanced web developers (HTML, CSS, JavaScript, React, Node.js, etc.) 
• Software developers with knowledge of Python or Node.js 
• Familiarity with APIs and databases is recommended 

Prerequisites to join our GenAI Standard course

Candidate must have good knowledge of these contents. 

• Good knowledge of programming languages such as HTML, JavaScript, React, and Node.js is desirable 
• No prior AI or Gen AI development knowledge is needed 

Why to learn Generative AI?

In the era of AI, generative AI or Gen AI is an emerging industry and it will be a game changer. Traditional website and software development is getting an overhauling after introduction of AI in 2022. Companies are developing better websites and applications which are carrying AI packed features. Gen AI is used to develop these features for apps and software. 

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

Know why  it is the best GenAI course?

Our well-researched and high-quality syllabus make it the best course to join with ADMEC. Mr. Ravi who is a senior programmer and researcher with 25 years of experience is the mentor. 

This course is available in classroom and online modes. Join this course as per your convenience. Our online classes are not video based or full of hundreds of students in a batch. 

Contents covered in this GenAI course

Syllabus of Gen AI training program

Module 1: Gen AI Introduction and Data Analysis Using Python 

• Introduction to Gen AI 
• Gen AI Course Route Map 
• 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

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

Module 3: Explore NLP 

• 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

• 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

• 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 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

• 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

• 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

• 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/low-code Workflow Automation Platforms

These platforms are very popular in many sectors to build AI tools and workflow without any code or low code. This module covers below given no-code workflow automation platforms.

• n8n
• make.com
• zapier
• autocad
• pipedream

More about Gen AI Syllabus

• NLP – It’s the core of this course. 
• Python – It is a language we need to build the Gen AI apps. 
• Statistics – Statistics is quite important in any Generative AI (Gen AI) course because Gen AI is built on the principles of data, probability, and inference—all of which are available in statistics. 
• DL – Understanding of DL model is required because you are going to train them only, Advanced knowledge of deep learning of DL is not mandatory to join this course. 
• ML & DL – Deep knowledge is not required to learn this course. But can be a requirement while working because you might get projects from these areas too. 

Career options after Gen AI development training 

Many career options open-up automatically as you complete your advanced Gen AI course for web and software development with ADMEC Multimedia, Delhi.  

Some of the main are: 

• Gen AI developer
• Python developer
• Django expert
• AI prompt engineer 
• AI product developer 
• LLP application manager 
• AI integration specialist 
• NLP engineer 
• Fine tuning specialist, etc.

Similar Courses to Join at ADMEC in Web, Data, ML & GenAI

• Advanced GenAI course for All
• Machine Learning and AI Diploma 
• Advanced Data Analytics Diploma 
• Entry Level Diploma in Data Analytics 
• Statistics Course 
• Python Certification Program 
• Node.js Certification Program