All Courses
Sign In
0

Generative AI Champion

Shaping the Future with Generative AI | Cutting-edge Techniques for Innovation

  • World-class Training Partner for AI Enthusiasts and Professionals
  • Earn 40 Learning Hours | 10 Practical Projects | 1000+ AI-focused Questions
  • Master Generative AI Applications Across Industries
  • Comprehensive Cheat Sheets | Step-by-step Learning Plan
  • Guaranteed to Run 20+ Live Cohorts in the Next 60 Days
View training options Talk to our advisor

Course Overview

eVani's Full Stack Web Developer Champion Program equips participants with comprehensive skills in both front-end and back-end development. The curriculum covers HTML, CSS, JavaScript, Angular, React, Node.js, Express, and MongoDB, with hands-on labs and real-world projects. Designed for aspiring developers and IT professionals, the program includes practical experience and career support, preparing students for industry certifications and impactful roles in web development.

Course Description

The Generative AI Champion Program is a rigorous four-month course designed to build advanced skills in generative AI and data analytics, blending theoretical knowledge with hands-on experience. Participants will explore Python, machine learning, natural language processing, deep learning, and cloud technologies, while working on real-world projects such as web scraping, customer churn prediction, Generative Adversarial Networks (GANs), LangChain, LlamaIndex, RAG, Vector DB, Graph DB, and chatbot development. This program equips individuals with the expertise required for careers in AI development, data science, and related fields, providing a comprehensive foundation and practical skills for a successful career in these dynamic industries.

Course Outcomes

understand and implement machine learning algorithms for practical applications.
Learn text pre processing, classification, and semantic analysis in NLP.
Explore and build models using autoencoders and GANs in deep learning.
Study large language models like GPT and BERT and develop practical projects.
Develop LLM-powered applications using LangChain.
Gain hands-on experience with Auto Gen and crew AI agents.
Build RAG models for document retrieval and question answering.
Use vector and graph databases for storing and retrieving data.
Explore and deploy generative AI models on cloud platforms.
Utilize ChatGPT to increase productivity in our day-to-day life.
Explore better LLMs other than ChatGPT like LLAMA 3, CLAUDE 3.5, Gamma.
Enhance any domain using Generative AI while maintaining data security with advanced methods.
Covers: HR, Office Administration, Finance, Risk Management, Retail, Sales & Marketing, Healthcare & Customer Support.

Pre-requisite

Basic understanding of programming concepts.
Familiarity with Python is beneficial.
Fundamental knowledge of statistics and mathematics.
Prior exposure to data analysis tools like Excel or SQL is a plus.
Willingness to engage in hands-on projects and practical exercises.
Any curious person irrespective of any domain can join us.

Target Audience

Aspiring data scientists and AI enthusiasts.
Professionals looking to transition into AI and data analytics roles.
Students and graduates in computer science, engineering, or related fields.
Business analysts and IT professionals seeking to enhance their data skills.
Anyone interested in learning about generative AI and its applications.
Welcoming students, IT experts, IT professionals, business leaders, and managers.

Download Boucher

This course includes:

  • Duration 100 hours

  • Lectures 150 lessons

  • Certificate of Completion

  • Full lifetime access

Upcoming Cohort Class

  • 14th Jan - 14th Mar Daily
  • Morning 10:38 AM
  • Sai Teja
View Schedule
41% Off
₹50,000.00
₹1,20,000
Enroll Now

Seats filling fast

Talk to Course Manager

Course Curriculum

  • 1 Generative AI Overview.
  • 2 Generative AI Principles
  • 3 Types of Generative Models
  • 4 Applications of Generative Models.
  • 5 Applications of Generative AI
  • 6 Advantages and Disadvantages of Generative AI.
  • 7 Ethical Considerations.
  • 1 Basics of python
  • 2 Data types.
  • 3 Conditional statements.
  • 4 Data Structures – String, List, Tuple, Set, Dictionaries
  • 5 Functions, Modules and Packages
  • 6 Object Oriented Programming and Exceptional Handling.
  • 7 File Handling
  • 8 Data Structures using Pandas and Mathematical Operations using NumPy.
  • 9 Data analysis using matplotlib and seaborn
  • 10 Web Scraping
  • 11 Project: Web scraping Flipkart products and performing data visualizations.
  • 1 Understanding the Data
  • 2 Probability and its uses
  • 3 Statistical Inference
  • 4 Data Clustering
  • 5 Data Testing
  • 6 Regression Modelling
  • 1 Artificial Intelligence & Data Science Overview
  • 2 Machine Learning Overview
  • 3 Data Preprocessing
  • 4 Exploratory Data Analysis.
  • 5 Supervised Learning – Linear, Logistic, Naïve Bayes, SVM, Decision Tree, Random Forest.
  • 6 Dimensionality Reduction – PCA .
  • 7 Unsupervised Learning – K-Means. KNN, Hierarchy Clustering.
  • 8 Associative Rule Mining – Eclat, Apriori.
  • 9 Model Selection and Boosting – XGBoost, k-Fold.
  • 10 Time Series Analysis.
  • 11 Reinforcement Learning.
  • 12 MLOps.
  • 13 Project: Churn customer prediction on telecom data.
  • 1 Introduction to Deep Learning.
  • 2 Tensor Flow Basics.
  • 3 Neural Network Fundamentals.
  • 4 Convolutional Neural Networks (CNN).
  • 5 Recurrent Neural Networks (RNN).
  • 6 Boltzmann Machine.
  • 7 Autoencoders
  • 8 Generative Adversarial Network (GAN)
  • 9 Exploring training techniques for GANs – gradient penalty, spectral normalization.
  • 10 Project: Generating hand written digits using GAN
  • 1 Natural Language Processing Overview.
  • 2 Text Preprocessing & Text Classification.
  • 3 Analyzing sentence structure.
  • 4 Semantic Analysis.
  • 5 Project: Sentiment Analysis
  • 1 Prompt Engineering Principles.
  • 2 What is Prompt Engineering?
  • 3 Why Prompt Engineering?
  • 4 Importance and Applications.
  • 5 Prompt Design Strategies.
  • 6 Few shots learning.
  • 7 Chain of thoughts(Advanced prompting techniques).
  • 8 Optimizing prompts .
  • 9 Project: Evaluating and increasing the accuracy of working chatbot.
  • 1 Large Language Model Overview.
  • 2 Large Language Model Types.
  • 3 Large Language Model Applications.
  • 4 Transformer Architecture.
  • 5 Transformer Models.
  • 6 GPT – Open AI.
  • 7 BERT – Google.
  • 8 Open-source models (mistral, llama3,etc...).
  • 9 Hugging Face.
  • 10 Closed source models(gemini,gpt3,claude,etc...).
  • 11 Introduction to streamlit for frontend development.
  • 12 Project: Creating a professional healthcare chatbot.
  • 1 Large Language Model Overview.
  • 2 LangChain Foundations.
  • 3 Benefits of using LangChain.
  • 4 Using LangChain to Develop LLM Applications.
  • 5 Value Propositions of LangChain.
  • 6 Components of LangChain.
  • 7 Off-the-Shelf Chains in LangChain.
  • 8 Build and Deploy LLM-Powered Applications using LangChain.
  • 9 Project: Travel planning bot using LangChain.
  • 1 Large Language Model Overview.
  • 2 Introduction to AutoGen.
  • 3 Complete hands on the AutoGen agents.
  • 4 Creating multiple agents in AutoGen.
  • 5 Complete hands-on crew ai agents.
  • 6 Deploying crew ai agents.
  • 7 LangChain vs AutoGen vs Crew ai.
  • 8 Project: Creating multiple agents using AutoGen and crew ai for and education bot and do evaluation for the same.
  • 1 Understanding Retrieval-Augmented Generation (RAG).
  • 2 Document Loading and Splitting.
  • 3 Vector Stores and Embeddings.
  • 4 Retrieval.
  • 5 Question Answering with Chatbots.
  • 6 Building RAG Models.
  • 1 Introduction to Vector Database.
  • 2 Vector Database Use case.
  • 3 Text Embedding.
  • 4 SQLite database.
  • 5 Storing and retrieving vector data in SQLite.
  • 6 Graph data bases.
  • 7 Exploring neo4j graph database.
  • 8 Converting text and storing into graph databases.
  • 9 Project: Storing a Wikipedia page into vector and graph databases.
  • 1 Understanding Retrieval-Augmented Generation (RAG).
  • 2 Understanding Graph data bases.
  • 3 Exploring node4j libraries.
  • 4 Advanced rag with optimized prompting
  • 5 Data masking.
  • 6 Project: Creating a finance bot to get which gives complete end to end finance organizing while maintaining data security.
  • 1 Introduction to Generative AI on Cloud.
  • 2 AWS Cloud Services for Generative AI.
  • 3 Azure Cloud Services for Generative AI.
  • 4 GCP Cloud Services for Generative AI.
  • 1 Introduction to fine tuning.
  • 2 Reasons to finetune.
  • 3 Methods of fine tuning.
  • 4 Using multiple agents to fine tune.
  • 5 Project: Fine tuning an LLM using multiple agents.
  • 1 Understanding the basics of ChatGPT and its capabilities.
  • 2 Overview of GPT- 4 architecture and its evolution to ChatGPT.
  • 3 Implementing ChatGPT for customer support.
  • 4 Exploring Gemini
  • 5 Exploring Claude
  • 6 Project: Enhancing chatbot responses with domain-specific training data.
  • 1 Intro to E-commerce with AI.
  • 2 Customer Support Emails with ChatGPT.
  • 3 Techniques for image augmentation to enhance visual assets.
  • 4 Various Business Domains
  • 5 Implementing in Product Development
  • 6 Project: Transforming Product Image Generation with Midjourney.
  • 1 Automating recruitment processes with AI.
  • 2 Enhancing employee engagement and retention using AI tools.
  • 3 Streamlining administrative tasks with AI automation.
  • 4 Project: Developing a chatbot for candidate screening and interview scheduling.
  • 1 Utilizing AI for financial forecasting and analysis.
  • 2 Implementing AI tools for investment decision-making.
  • 3 Leveraging AI for fraud detection and prevention.
  • 4 Project: Using AI to predict market trends and investment opportunities
  • 1 Enhancing customer experiences with AI-driven solutions.
  • 2 Implementing AI for inventory management and demand forecasting.
  • 3 Using AI to optimize sales strategies and lead generation.
  • 4 Project: Developing targeted marketing campaigns using AI
  • 1 Utilizing AI for medical diagnosis and treatment recommendations.
  • 2 Implementing AI for patient monitoring and care.
  • 3 Enhancing customer service with AI chatbots and virtual assistants.
  • 4 Automating customer query resolution.
  • 5 Project: Developing a chatbot for healthcare consultations.

Why Choose Us?

Excellent Mentors: Industry-experienced instructors with deep expertise in their respective fields.
Hands-on Projects: Practical hands-on projects and assignments that reinforce learning and facilitate skill application.
Customer Success: Dedicated customer support and learning assistance throughout the learning journey.
Lifetime Access: Lifetime access to course materials and updates, ensuring continuous learning and skill enhancement. One-time investment for lifetime access.
Cost-Effective Solutions: Affordable pricing models to fit varying budgets while maintaining high standards of education.

Hiring Companies

Learning Path

Step 1
Step 2
Step 3
Step 4
Step 5
Step 5

Shopping Cart

Loading...