📈 Your Market Value After This Course
What you'll achieve and how much you can earn after completing GenAI & ML
Fresher / Entry Level
₹7 - 10 LPA
0-2 years experience
- Junior GenAI Engineer
- AI Trainee
⭐ Most Common
Mid Level
₹12 - 25 LPA
2-5 years experience
- Generative AI Engineer
- LLM Specialist
Senior / Expert Level
₹30 - 60+ LPA
5+ years experience
- Lead AI Researcher
- GenAI Architect
🎯 Job Roles You Can Apply For
Generative AI Engineer
LLM Engineer
Machine Learning Engineer
AI Engineer
Prompt Engineer
NLP Engineer
⚡ Skills You'll Master
Python
Machine Learning
Deep Learning
Generative AI
LLMs (GPT, Llama)
LangChain
RAG
Prompt Engineering
Transformers
NLP
📚 Complete Course Syllabus
Master every aspect with our comprehensive curriculum
Module 1: Python for AI/ML
- Python Basics - Variables, Data Types, Operators
- Control Flow - if-else, for, while loops
- Functions & Lambda Expressions
- Data Structures - Lists, Tuples, Dictionaries, Sets
- NumPy - Arrays & Mathematical Operations
- Pandas - Data Manipulation & Analysis
- Object-Oriented Programming in Python
- Working with APIs - requests library
- Async Programming - asyncio
Module 2: Mathematics & Statistics
- Linear Algebra - Vectors, Matrices
- Matrix Operations - Dot Product, Transpose
- Calculus - Derivatives, Gradients, Backpropagation
- Probability - Conditional Probability, Bayes Theorem
- Statistics - Mean, Median, Mode, Variance
- Distributions - Normal, Binomial, Poisson
- Hypothesis Testing & p-values
Module 3: Machine Learning Fundamentals
- Introduction to ML - Types of Learning
- Linear Regression - Simple & Multiple
- Logistic Regression for Classification
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Clustering - K-Means, Hierarchical
- Ensemble Methods - XGBoost, LightGBM
- Model Evaluation - Cross-Validation, Metrics
Module 4: Deep Learning & Neural Networks
- Neural Networks - Perceptron, Activation Functions
- Forward & Backward Propagation
- Optimizers - SGD, Adam, RMSprop
- TensorFlow & Keras Basics
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- LSTM & GRU Networks
- Transformers Architecture
- Attention Mechanism & Self-Attention
- Transfer Learning & Fine-tuning
Module 5: Natural Language Processing (NLP)
- Text Preprocessing - Tokenization, Stemming, Lemmatization
- Stopwords Removal & Punctuation Cleaning
- Bag of Words & TF-IDF
- Word Embeddings - Word2Vec, GloVe, FastText
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- BERT & Transformer-based Models
Module 6: Introduction to Generative AI
- What is Generative AI - History & Evolution
- Generative vs Discriminative Models
- Applications of Generative AI
- Generative Models - GANs, VAEs
- Ethics & Challenges in GenAI
Module 7: Large Language Models (LLMs)
- What are Large Language Models (LLMs)
- How LLMs Work - Transformers & Attention
- Popular LLMs - GPT, Llama, Claude, Gemini
- OpenAI API - API Keys, Pricing, Rate Limits
- GPT-3.5, GPT-4, GPT-4 Turbo
- Open Source Models - Llama 2, Mistral, Falcon
- Working with Hugging Face Transformers
- LLM Limitations - Hallucinations, Bias, Context Window
Module 8: Prompt Engineering
- What is Prompt Engineering
- Zero-shot vs Few-shot Prompting
- Chain-of-Thought (CoT) Prompting
- Tree-of-Thoughts (ToT) Prompting
- Role-based Prompting
- Advanced Prompt Techniques
- Prompt Injection & Security
- Best Practices for Prompt Engineering
Module 9: LangChain Framework
- Introduction to LangChain
- LangChain Components - LLMs, Prompts, Chains
- LCEL (LangChain Expression Language)
- Chains - Sequential, Router, Transform
- Memory in LangChain
- Integration with LLMs - OpenAI, Hugging Face
- Building Applications with LangChain
Module 10: RAG (Retrieval-Augmented Generation)
- What is RAG (Retrieval-Augmented Generation)
- RAG Architecture - Retriever, Generator
- Vector Databases - Chroma, Pinecone, Weaviate
- Embeddings - Text Embeddings, OpenAI Embeddings
- Document Loaders & Splitters
- Retrieval Strategies - Similarity Search, MMR
- Building RAG Applications with LangChain
- Advanced RAG Techniques
Module 11: Fine-tuning LLMs
- What is Fine-tuning - Why & When
- Fine-tuning vs Transfer Learning
- OpenAI Fine-tuning API
- Parameter Efficient Fine-tuning (PEFT)
- LoRA (Low-Rank Adaptation)
- QLoRA - Quantized LoRA
- Fine-tuning Open Source Models
- Best Practices for Fine-tuning
Module 12: AI Agents & Tools
- Introduction to AI Agents
- Agent Architectures - ReAct, Plan-and-Execute
- Tools & Function Calling
- Building Agents with LangChain
- AutoGPT & BabyAGI
- Multi-Agent Systems
Module 13: GenAI Model Deployment
- Deploying ML Models with FastAPI/Flask
- Deploying LLM Applications
- Streamlit for AI Apps
- Gradio for ML Demos
- Cloud Deployment - AWS, GCP, Azure
- Docker for GenAI Applications
Module 14: Real-World Projects
- Project 1: AI-Powered Chatbot
- Project 2: Document Q&A System (RAG)
- Project 3: Text-to-SQL Application
- Project 4: Code Generation Assistant
- Project 5: Sentiment Analysis Tool
- Project 6: Image Captioning Generator
- Capstone Project - End-to-End AI Application
⭐ Why Choose Tekksol Global?
We provide the best learning experience with industry experts
Expert Trainers
Learn from industry professionals with 10+ years of AI/ML experience
Hands-on Projects
Work on 8+ real-time GenAI and ML projects
Industry Certification
Get globally recognized GenAI certification
100% Placement Support
Tie-ups with 320+ companies for AI/ML roles
Resume Building
Professional resume & portfolio with AI projects
Mock Interviews
Regular mock interviews with detailed feedback
💻 Real-Time Projects
Build impressive portfolio with industry-relevant projects
AI-Powered Chatbot
Build an intelligent chatbot using GPT API and LangChain with conversation memory and custom knowledge base.
Python
OpenAI API
LangChain
Streamlit
Document Q&A with RAG
Create a document question-answering system using RAG architecture with vector database for PDF documents.
LangChain
Chroma
OpenAI
Streamlit
Code Generation Assistant
Develop a code generation assistant using LLMs to generate, explain, and debug code in multiple programming languages.
Python
OpenAI API
Gradio
LangChain
🚀 Placement Assistance
We're committed to your success beyond the course
Placement Support Includes:
- Resume & LinkedIn Profile Building
- Aptitude & Technical Training
- Mock Interviews with Industry Experts
- Soft Skills & Communication Training
Our Hiring Partners:
- 500+ Hiring Partners
- Unlimited Interview Opportunities
- Job Portal Access
- Life-long Placement Support
Our Top Hiring Partners
❓ Frequently Asked Questions
Got questions? We've got answers
What are the prerequisites for GenAI course?
Basic programming knowledge is helpful. We cover Python from scratch, so no prior AI/ML experience required.
What is the duration of the course?
The course duration is 4 months (140 hours) with flexible batch timings.
Will I learn both ML and GenAI?
Yes, the course covers traditional ML, Deep Learning, NLP, and Generative AI with LLMs.
What projects will I build?
You will build 8+ projects including Chatbots, RAG Systems, Code Assistant, and more.
Is placement assistance provided?
Yes, we provide 100% placement assistance with 320+ hiring partners.
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