Generative AI Specialist (Batch-01)

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What Will You Learn?

  • Master the creation of advanced generative AI applications using the Langchain framework and Huggingface's cutting-edge models.
  • Understand the architecture and design patterns for building robust and scalable generative AI systems.
  • Gain practical experience in deploying generative AI models across various environments, including cloud platforms and on-premise servers.
  • Explore deployment strategies that ensure scalability, reliability, and optimal performance of AI applications.
  • Develop Retrieval-Augmented Generation (RAG) pipelines to boost the accuracy and efficiency of generative models by integrating retrieval mechanisms.
  • Seamlessly incorporate Huggingface's pre-trained models into Langchain applications to leverage their powerful NLP capabilities.
  • Customize and fine-tune Huggingface models to meet specific application needs and use cases.
  • Engage in real-world projects demonstrating Generative AI applications in domains such as chatbots, content generation, and data augmentation.

Course Content

Class 01 – Introduction of Generative AI | AI Future Prospects | Career Opportunities | Training Modules | Instructor Introduction | QnA Session

  • Demo Session
    02:18:42

Class 02 – Basics of Python (Punctuations, Mathematical Operators, Brackets and Craces, Logical and Comparison) | Syntax and Semantics | Conditional Statement | Python Variables (Variables, Declaring and Assigning Variables, Naming Conventions, Types) | Practical Examples and Common Errors

Class 03 – Data Types (Integers, Floating, Point Numbers, Strings, Booleans) | Advanced Data Types (Lists, Tuples, Sets, Dictionaries) | Operators (Arithmetic, Comparison, Logical) | Conditional Statements (IF, ELIF, ELSE) | Practical Example – Employees Bonus Calculator

Class 04 – Loops (For Loop and While Loop) | Iterating over Range | Iterating over String | Loop Control Statements (Break, Continue, Pass) | Nested Loops | Data Structure (List, Tuples, Sets, Dictionaries) | List Comprehension

Class 05 – List Practical Examples (Managing an Inventory, Collecting Feedback) | Data Structure (Tuples) – Creating, Assessing and Operations | Mutable vs Immutable Tuples | Methods | Packing and Unpacking Tuples | Nested Tuples

Class 06 – Data Structure (Dictionaries) – Creating, Assessing and Modifying | Methods | Looping over Dictionaries | Nested Dictionaries | Dictionaries Comprehensions | Functions (Introduction to Functions, Defining Functions, Calling Functions, Parameters, Variable-Length Arguments, Return Statements)

Class 07 –

Class 08 – Python Libraries Overview (Array, Math, OS, Random, Shutil, JSON, CSV, Datetime) | File Operations (Read and Write Files) | Working with File Paths | Object-Oriented programming (OOP)

Class 09 – Object-Oriented programming (OOP) Basic | Inheritance in Python (Single and Multiple) | Polymorphism | Polymorphism with Abstract Base Classes

Class 10 – Polymorphism with Abstract Base Classes (Interfaces) | Encapsulation (Protected Method)

Class 11 – Encapsulation with Getter and Setter (Public, Protected, Private Variables or Access Modifiers)

Class 12 – Theory: One Hot Encoding | BOW | TF-IDF | Word2Vec | ANN (Artificial Neural Network) | CNN (Convolutional Neural Network) | Classification

Class 13 –

Class 14 –

Class 15 – Prediction and Deployment | Churn Modelling | ANN Implementation | Label Encoder Gender | One-Hot Encoder

Class 16 – Salary Regression | Encode Categorical Variables | One-Hot Encode | ANN Regression | Regression Model | Set Up TensorBoard

Class 17 – LSTM RNN | LSTM Architecture | Forget Gate in LSTM | Understanding LSTM Networks

Class 18 – Input Gate and Candidate Emory | Output Gate in LSTM | Training Process in LSTM RNN | GRU RNN Intuition