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Mastering Generative AI and ChatGPT

Self-Paced Course
interested count5k+ interested Geeks

Course Description

Embark on our Generative AI course! Learn to creatively generate content with expert guidance. Gain practical skills for real-life applications. Whether you're new or experienced, unleash your creativity with us!

levelBeginner to Advancecourse duration5 Weeks

GFG Karlo, Hojayega!
Flat 25% Discount | Limited Time Offer

Course Overview

  • Interactive Learning: Engage in hands-on sessions exploring Language Models and Generative AI concepts.
  • 8 hrs+ Hands-On Learning: Dive deep into practical exercises to reinforce your understanding.
  • Notes, MCQs & Contests: To Practice & Test your Knowledge
  • Expert Guidance: Learn from industry experts sharing insights and best practices.
  • Certificate of Completion: Earn recognition for mastering LLMs and Generative AI concepts.
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Course Content

01Overview to Generative AI
  • Overview of Generative Al
  • Historical Context and Evolution
  • Basic machine learning: supervised, unsupervised, and reinforcement learning
  • Introduction to Deep Learning and Neural Networks
02Foundational Concept of AI

Supervised learning

  • Naive Bayes Classifier
  • K Nearest Neighbor Classifier
  • Decision Tree Classifier
  • Support Vector Machine (SVM Classifier)
  • Logistic Regression Classifier
  • Linear Regression
  • Lasso & Ridge Regression

Unsupervised Learning

  • K Means Clustering
  • Agglomerative Clustering
  • FP growth method

Reinforcement Learning

  • Overview of Reinforcement Learning

Machine Learning Projects

  • Projects of Machine Learning
03Introduction to Deep Learning and Neural Network

Deep learning

  • Basics of Neuron
  • Neural Networks
  • Types of Neural Networks
  • Types of Architecture in Deep Neural Networks
  • Practical coding implementation using neural networks
  • Creating different types of architecture
04Introduction to Generative AI
  • Introduction to Generative Al
  • Self Attention models
  • single head self attention model
  • multi-head self attention model


  • Transformers
  • Positional Encoding
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