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Generative AI Course - Learnees

Generative AI Course

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A Generative AI course introduces learners to AI models that create new content such as text, images, audio, and code. It covers foundational concepts, model architectures, and practical hands-on skills to build and apply generative tools in real-world scenarios.

Trainer

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Course started on:

Upto First 3 Sessions - 100% Refund Guarantee

1. Streamlit + Python Essentials

Topics:

Introduction to Python:variables datatype control structures
Functions modules error handling
Numpy and Pandas basics for data manipulation
Getting started with Streamlit
Using widgets layout and components
Creating interactive dashboards and LLM UI mockups

Hands-on:

Skills You Will Learn:

2. Machine Learning Foundations

Topics:

Understanding supervised and unsupervised learning
Scikit-learn model building: Linear & Logistic Regression
Train-test split cross-validation
Hyperparameter tuning and evaluation metrics

Hands-on:

Skills You Will Learn:

3. Deep Learning with PyTorch

Topics:

Neural network basics: perceptron multi layer networks
Introduction to PyTorch tensors and autograd
Building simple neural networks using nn.Module
Training loop with loss functions and optimizers
GPU acceleration and model evaluation

Hands-on:

Skills You Will Learn:

4. NLP Fundamentals

Topics:

Text: cleaning stemming lemmatization
Vectorization using Bag of Words TF-IDF Word2Vec
Sequence models: RNN LSTM concepts
Limitations of traditional NLP techniques

Hands-on:

Skills You Will Learn:

5. Transformers & Hugging Face

Topics:

Transformer architecture (self-attention encoder-decoder)
BERT GPT-2 overview and use cases
Using Hugging Face’s transformers pipeline
Downloading and using pre-trained models
Hands-on: sentiment classifier text summarizer

Hands-on:

Skills You Will Learn:

6. LangChain Essentials

Topics:

LangChain architecture:Chains Agents Memory
Building LLM chains using prompts and tools
reating a conversational bot with LangChain
Integration with OpenAI API
LangChain toolkits: calculator Python REPL

Hands-on:

Skills You Will Learn:

7. RAG with Hugging Face & LangChain

Topics:

Understanding RAG and how it enhances LLM's
Vector databases: FAISS Pinecone basics
Document loading and chunking for retrieval
Creating context-aware Q&A systems
Build a private chatbot using LangChain + RAG

Hands-on:

Skills You Will Learn:

8. Deployment & Optimization

Topics:

Prompt engineering patterns and anti-patterns
Using Streamlit Cloud and Hugging Face Spaces for deployment
Monitoring LLM behavior and optimization techniques
Caching
truncation rate limits and usage metrics
Addressing hallucinations bias and safety in LLMs

Hands-on:

Skills You Will Learn:

9. Capstone Project

Topics:

Plan and design a complete GenAI solution
Possible projects: Resume screener PDF-based chatbot AI tutor
Incorporate Streamlit + LangChain + RAG
Model evaluation and improvement
Presentation and deployment

Hands-on:

Skills You Will Learn:

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First 3 Sessions - 100% Refund

What You'll Learn in Generative AI Course

Master the essential skills and industry-leading tools that will prepare you for real-world challenges and career success

Python programming

Machine learning basics

Linear algebra

Data preprocessing

Deep learning fundamentals

Neural networks

Prompt engineering

Model evaluation and cloud computing basics.

Curriculum Designed by Industry Experts

Master the skills employers are looking for with our comprehensive, hands-on curriculum

Streamlit + Python Essentials

6 Topics

Machine Learning Foundations

4 Topics

Deep Learning with PyTorch

5 Topics

NLP Fundamentals

4 Topics

Transformers & Hugging Face

5 Topics

Free Career Counselling

We're happy to help you 24/7

1. Streamlit + Python Essentials

Topics:

Introduction to Python:variables datatype control structures
Functions modules error handling
Numpy and Pandas basics for data manipulation
Getting started with Streamlit
Using widgets layout and components
Creating interactive dashboards and LLM UI mockups

Hands-on:

Skills You Will Learn:

2. Machine Learning Foundations

Topics:

Understanding supervised and unsupervised learning
Scikit-learn model building: Linear & Logistic Regression
Train-test split cross-validation
Hyperparameter tuning and evaluation metrics

Hands-on:

Skills You Will Learn:

3. Deep Learning with PyTorch

Topics:

Neural network basics: perceptron multi layer networks
Introduction to PyTorch tensors and autograd
Building simple neural networks using nn.Module
Training loop with loss functions and optimizers
GPU acceleration and model evaluation

Hands-on:

Skills You Will Learn:

4. NLP Fundamentals

Topics:

Text: cleaning stemming lemmatization
Vectorization using Bag of Words TF-IDF Word2Vec
Sequence models: RNN LSTM concepts
Limitations of traditional NLP techniques

Hands-on:

Skills You Will Learn:

5. Transformers & Hugging Face

Topics:

Transformer architecture (self-attention encoder-decoder)
BERT GPT-2 overview and use cases
Using Hugging Face’s transformers pipeline
Downloading and using pre-trained models
Hands-on: sentiment classifier text summarizer

Hands-on:

Skills You Will Learn:

6. LangChain Essentials

Topics:

LangChain architecture:Chains Agents Memory
Building LLM chains using prompts and tools
reating a conversational bot with LangChain
Integration with OpenAI API
LangChain toolkits: calculator Python REPL

Hands-on:

Skills You Will Learn:

7. RAG with Hugging Face & LangChain

Topics:

Understanding RAG and how it enhances LLM's
Vector databases: FAISS Pinecone basics
Document loading and chunking for retrieval
Creating context-aware Q&A systems
Build a private chatbot using LangChain + RAG

Hands-on:

Skills You Will Learn:

8. Deployment & Optimization

Topics:

Prompt engineering patterns and anti-patterns
Using Streamlit Cloud and Hugging Face Spaces for deployment
Monitoring LLM behavior and optimization techniques
Caching
truncation rate limits and usage metrics
Addressing hallucinations bias and safety in LLMs

Hands-on:

Skills You Will Learn:

9. Capstone Project

Topics:

Plan and design a complete GenAI solution
Possible projects: Resume screener PDF-based chatbot AI tutor
Incorporate Streamlit + LangChain + RAG
Model evaluation and improvement
Presentation and deployment

Hands-on:

Skills You Will Learn:

Why Choose LEARNEES

Discover the unique advantages that make our learning platform stand out

24x7 Support

One-On-One Learning Assistance
Help Desk Support
Resolve Doubts in Real-time

Hands-On Project Based Learning

Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments

Industry Recognised Certification

Learnees Training Certificate
Graded Performance Certificate
Certificate of Completion

Generative AI Course Certification

Master Skills & Earn Your Professional Certificate

Industry-recognized certificate by Learnees Institute

Learn from certified trainers with hands-on sessions

Receive career support and placement guidance

Globally recognized certification for career growth

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Career Opportunities

Job roles are being prepared for this course. Please check back later.

Why enroll for Generative AI Course Certification?

Unlock your potential in with Generative AI Course certification

Most Popular Certification

Generative AI Course certification is rated as one of the most popular and lucrative cloud certifications in IT globally.

Growing Market

The global Generative AI Course market size is expected to grow at a CAGR of 21.2% from 2024 to 2030.

High Salary Potential

The estimated total pay for an Generative AI Course is ₹7,09,000 per year, with an average salary of ₹6,09,000 per year - Glassdoor.

Course Overview: Generative AI Course

Essential details: objectives, prerequisites, schedule, and outcomes

This course aims to equip learners with a solid understanding of generative AI concepts, models, and real-world applications. It enables participants to build, evaluate, and deploy generative models such as GPT, diffusion models, and VAEs. Learners will develop practical skills to create AI-driven content responsibly while understanding ethical, technical, and industry considerations.