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Master Generative AI

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Master Generative AI

About The Course

Advanced Certification Programme in AI, ML, Data Science, and Generative AI

COURSE OVERVIEW

This course provides a deep dive into the fundamentals and advanced concepts of Artificial Intelligence (AI), Machine Learning(ML), Data Science, and Generative AI. From understanding the basics to implementing complex algorithms, this course covers everything you need to know to excel in these fields.Entire course will be driven by Gen AI tools like cody, codegpt, copilot, gemni pro..etc.

  • Introduction
  • Python and AI/ML Basics
  • Data Preparation & Augmentation
  • Model Selection & Architecture Design
  • Training, Evaluation, & Fine-tuning
  • Generation, Integration, & Testing
  • Deployment, Monitoring, & Maintenance

ADDITIONAL RESOURCES

 

We Encourage students to work on real-world projects and participate in hands-on workshops to apply their learning and reinforce theoretical concepts.

 

Hands-on Workshops

We Believe “Knowledge sharing is caring ”

Course Content

Introductions: (Days 1 to 10)

  • Course Overview & Importance of AI/ML
  • GenAI Structure and Goals
  • What is AI, ML, Data Science, and Generative AI?
  • History and evolution of AI, ML, and Generative AI.
  • Importance and applications of AI, ML, Data Science, and Generative AI in various industries.
  • Python Basics for AI/ML
  •  Introduction to NumPy, Pandas, Matplotlib,scikit-learn
  • Deep Learning with PyTorch.
  •  Overview of AI/ML Fundamentals

Python and AI/ML Basics: (Days 11-23)

  • Fundamentals of AI & ML
  • Overview of supervised, unsupervised, and reinforcement learning.
  • Understanding algorithms: decision trees, linear regression, logistic regression, etc.
  • Deep Learning for Generative AI The GenAI Tech Stack
  • Ethical Considerations in GenAI
  • Python Modules (NumPy, Pandas, Matplotlib)
  •  TorchText and Hugging Face Transformers
  •  PyTorch Core Library (torch, nn, optim, utils, nn.functional)

Data Preparation & Augmentation: (Days 24-30)

  • Data cleaning, transformation, and normalization.
  • Handling missing data and outliers.
  •  Feature selection and extraction.
  • Data Preprocessing with Python and PyTorch.
  • Prompt Engineering with Google AI Tools
  • Data Quality Assurance & OpenAI File Formats
  •  Data visualization tools and techniques.
  •  Exploratory Data Analysis (EDA).
  •  Communicating insights from data.

Model Selection & Architecture Design: (Days 31-39)

  •  Deep Dive into Generative AI Models in PyTorch
  •  Architecting for Prompt-based Language Models
  •  Deep Learning Algorithms for Text Generation
  •  Deep learning fundamentals: neural networks, CNNs, RNNs.
  •  Natural Language Processing (NLP) and its applications.
  •  Model Selection & Use Case Mapping

Training, Evaluation, & Fine-tuning: (Days 40-51)

  •  LLMOps on Google Cloud with Python & PyTorch
  •  Mastering Machine Learning Frameworks
  • Leveraging Introduction to Generative AI and its applications.
  • Generative models: GANs, VAEs, etc.
  • Creating art, music, and text with Generative AI.
  •  Introduction to Large Language Models (LLMs) and their applications.
  •  Retrieval-Augmented Generation (RAG) models and their use cases.
  • 4Prompt engineering techniques for improving LLM performance.
  •  OpenAI API & Fine-tuning Techniques
  •  Understanding & Implementing Optimization Algorithms
  • Model Evaluation & Performance
  •  Metrics Fine-tuning based on Evaluation

Generation, Integration, & Testing: (Days 52-58)

  • Generative Model Integration with Applications
  • Testing & Validation of Generated Text Content
  •  Advanced Prompt Engineering for Control

Deployment, Monitoring, & Maintenance: (Days 59-75)

  •  Production-Ready Deployment on Google Cloud
  •  LLMOps Monitoring & Bias Detection
  • Continuous Maintenance & Model Improvement
The Course Curriculam
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