1 Python Overview

• Wide Range of Applications:
  • Web Development, Data Science, Automation, Game Development, Desktop Applications.
• Career Opportunities:
  • Roles such as Python Developer, Data Scientist, Automation Engineer.
• Extensive Libraries and Frameworks:
  • Overview of Python's standard library and popular third-party libraries.
• Compilation Process:
  • Compiling Source code to Byte code (.pyc) and then using the interpreter from bytecode to execution using PVM
• Various implementation of Python

2 Set up environment

• IDLE: Integrated Development and Learning Environment.
• Github account:
• Fork repository:
• Using code cpaces:
• Installing required extensions:
• Jupyter notebook:
  • Setting up Jupyter notebooks.
  • Writing and executing Python code in notebooks.
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3 Basic Syntax Rules

• Indentation, variables, and comments.
• Naming conventions.
• Using the debugger
• Stdio interactivity.
• Simple and basic programs
• Variables declarations etc.

4 Variables, Data Types, and Type Conversion

• Working with different built-in data types (int, float, string, etc.).
• Type conversion and casting.
• Interning

5 Control structures

• if-then-else
• Looping
• For loop, while loop, do while
• Range, Break and Continue
• Nested loops, comprehensions, and conditionals.
• Working with complex logical conditions.

6 Comosite data types

• Packing and unpacking tuples
• Returning tuple from a function
• Copying a tuple
• Reversing a Tuple
• Comparing Tuples
• Loop a tuple
• Tuple in memory
• Set functions
• Set Methods
• Frozenset
• Using tuples as keys
• Create using dict comprehension
• Dictionary methods
• Loop over a dict
• Using specialized container datatypes like defaultdict, deque, Counter, etc.

7 Functions

• Defining functions, passing arguments, and returning values.
• Process Map of a program.
• Formal vs. Actual Parameters
• Annotations
• Positional and Keyword Arguments
• Variable number of arguments
• Function object (globals, name, class, call, etc.)
• Anonymous functions with lambda.

8 String formatting

• Strings as read-only arrays
• String concatenation
• String internal representation
• Interning ( Implicit Interning, Explicit Interning )
• String methods
• F-Strings for formatting strings.
• Replacement Field {} specification.
• The optional conversion operators “!”, ":".
• Fill + Align + Sign+Width.
• Grouping Options.
• The 0 (zero) Character
• Presentation Types & the character #

9 Modules and packages

• Creating and importing custom modules.
• Understanding Python packages and namespaces.
• Variations of import statement.
• How are modules loaded?

10 Namespaces and scopes

• Namespaces: understanding them as dictionaries
• Scopes
• Local, global, and nonlocal variables.
• Scope resolution and the global keyword.

11 Classes and OOPS

• Class vs. Functions
• Data Attributes of Classes
• Function Object vs Method Object
• Bound Method Object vs Function Object
• Initial State of a Class
• Play around with Class’s init
• What about init on instance object
• Calling methods/functions within an instance or a class.
• Creating classes dynamically.
• Metaclass

12 Classes and OOPS continued

• Single and multiple inheritance.
• Overriding methods.
• Check instance and class inheritance.
• Polymorphic functions and operator overloading.
• Using private and protected members.
• Abstract base classes.

13 Exception Handling

• Handling Exceptions
• Getting the instance of the Exception
• Raising Exceptions
• Another way to get the instance of the Exception
• Nesting Try…Except
• try-except-else
• User Defined Exceptions
• The finally block

14 File I/O

• Reading and writing files.
• Working with different file formats.

15 DUNDER & other useful stuff

• Special methods like __init__, __str__, __len__, and custom operator overloading.
• __new__()
• __call__()
• Using with statements and implementing custom context managers.

15 Multithreading

• Basics of parallel processing.
• Working with the threading and multiprocessing modules.

16 Async IO

• Introduction to asynchronous programming in Python.
• Using async and await for non-blocking code.

17 Decorators

• Creating and using decorators to modify function behavior.

18 Interators and generators

• Implementing custom iterators.
• Using generators for lazy evaluation.

2.3.1 Installing Packages with pip

• Installing and managing external libraries.

2.3.2 Data Analysis with pandas

• Introduction to pandas for data manipulation.

2.3.3 Web Scraping with BeautifulSoup and requests

• Extracting data from websites.
• Handling HTTP requests.

2.3.4 Working with APIs

• Making API calls and handling responses.

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2.4 Testing and Debugging

2.4.1 Debugging in Python

• Using debugging tools (pdb, breakpoints).

2.4.2 Unit Testing

• Writing and running tests with unittest.

2.4.3 Test-Driven Development (TDD)

• Introduction to TDD practices.

2.4.4 Continuous Integration (CI) Basics

• Setting up CI for automated testing.

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2.5 Python in Practical Applications

2.5.1 Python for Data Science and Machine Learning

• Introduction to NumPy:
  • Basics of numerical operations with NumPy.
• Data Visualization with Matplotlib and Seaborn:
  • Creating visualizations for data analysis.
• Introduction to scikit-learn:
  • Basic machine learning tasks.

2.5.2 Python for Web Development

• Introduction to Flask:
  • Building a simple web application.
• Introduction to Django:
  • Overview of Django for full-stack development.
• Working with Databases:
  • Basic database operations using SQLAlchemy or Django ORM.

2.5.3 Python in DevOps

• Automation with Python:
  • Writing scripts for task automation.
• Python and Docker:
  • Creating Docker containers for Python applications.
• Working with Cloud Services:
  • Using Python with cloud platforms like AWS, Azure.

2.6 Summary and Q&A

• Recap of the key topics covered in the session.
• Open floor for questions and clarification.

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Session 3: Introduction to LangChain and Prompting Techniques

Duration: 5-6 hours

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3.1 Introduction to LangChain

• 3.1.1 Overview of LangChain:

  • What is LangChain and why use it?
  • Key concepts: Chains, Agents, Prompts, and LLMs (Large Language Models).
  • Use cases: Applications in AI-driven workflows (text generation, Q&A systems, conversation agents, etc.).

• 3.1.2 Understanding LangChain Components:

  • Chains: Connecting various LLMs for complex tasks.
  • Agents: Dynamic decision-makers that use chains.
  • Prompts: Inputs that guide LLMs toward desired outputs.
  • Models: Types of LLMs that LangChain can interface with.

• 3.1.3 Hands-On Activity: Setting Up a LangChain Project

  • Step-by-step guide to setting up a new LangChain project environment.
  • Installing necessary packages and dependencies.
  • Configuring API keys and services (e.g., OpenAI, Cohere).

3.2 Demo: Creating a Simple Chain to Connect Multiple LLMs

• 3.2.1 Creating a Simple Chain:

  • Understanding the flow of data between multiple LLMs.
  • Building a basic chain to connect two or more LLMs for sequential tasks.
  • Practical example: Constructing a chain for content summarization followed by text generation.

• 3.2.2 Hands-On Exercise: Implementing a Basic Chain

  • Participants create a simple chain using the LangChain library.
  • Experiment with chaining different LLMs to handle tasks like translating text, generating summaries, and creating questions.

3.3 Introduction to Prompting Techniques

• 3.3.1 Foundations of Prompting:

  • What are prompts and why are they crucial?
  • Key elements of an effective prompt: Context, Instructions, and Expected Output.
  • Different types of prompts: Text generation, Q&A, summarization, data extraction, etc.

• 3.3.2 Hands-On Exercise: Writing Effective Prompts

  • Practice writing prompts for various tasks:
    • Text generation: Crafting creative and informative content.
    • Q&A: Designing prompts for factual responses.
    • Summarization: Creating prompts for concise summaries.
  • Participants refine prompts through trial and error with different models.

• 3.3.3 Demo: Testing and Refining Prompts with Different Models

  • Demonstration of how different models respond to various prompt types.
  • Techniques to adjust prompts for better performance and accuracy.
  • Using prompt tuning methods to handle ambiguous or complex tasks.

**3.4 Advanced Prompt Engineering **

• 3.4.1 Advanced Techniques in Prompt Design:

  • Overview of few-shot learning: Using multiple examples in prompts to guide LLMs.
  • Context management: Maintaining relevance and coherence in multi-turn conversations.
  • Handling user-specific and context-specific nuances.

• 3.4.2 Hands-On Activity: Implementing Few-shot Learning and Context Management

  • Participants implement few-shot learning prompts for specific tasks (e.g., text classification, decision making).
  • Practice techniques to manage context in long conversations or multi-turn dialogues.

**3.5 Demo: Building a Context-Aware Conversation Agent **

• 3.5.1 Creating a Context-Aware Agent:

  • Combining chains, agents, and prompts for dynamic interactions.
  • Designing an agent that adapts responses based on conversation history and context.
  • Key challenges: Memory management, avoiding repetition, maintaining engagement.

• 3.5.2 Live Demo: Context-Aware Agent in Action

  • Showcasing a conversation agent that uses advanced prompt engineering to handle various user inputs.
  • Discussing real-world applications of context-aware agents (customer service bots, virtual assistants, etc.).

• 3.5.3 Q&A and Wrap-up :

  • Open floor for questions, clarifications, and discussions.
  • Summary of key takeaways and additional resources for further learning.

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Session 4: Embedding and Vector Databases

Duration: 4-5 hours

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4.1 Understanding Embeddings

• 4.1.1 Overview of Embeddings:

  • What are embeddings and their role in Natural Language Processing (NLP)?
  • Types of embeddings: Word2Vec, GloVe, BERT, Sentence Transformers, etc.
  • Applications of embeddings in various NLP tasks (text similarity, clustering, classification).

• 4.1.2 Hands-On Activity: Generating Embeddings Using Different Models (30 minutes)

  • Generate embeddings using BERT, Sentence Transformers, and other popular models.
  • Practical example: Embedding sentences, paragraphs, and documents.
  • Comparing the embeddings generated by different models.

• 4.1.3 Demo: Visualizing Embeddings and Understanding Their Structure (15 minutes)

  • Visualizing high-dimensional embeddings using techniques like PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding).
  • Interpreting embedding clusters and distances.

4.2 Introduction to Vector Databases

• 4.2.1 Overview of Vector Databases:

  • What are vector databases, and why use them?
  • Key concepts: Vector similarity, approximate nearest neighbors (ANN), indexing.
  • Commonly used vector databases: Pinecone, FAISS, Milvus, etc.

• 4.2.2 Hands-On Activity: Setting Up a Vector Database

  • Step-by-step guide to setting up a vector database like Pinecone or FAISS.
  • Basic configuration and connection to your Python environment.

• 4.2.3 Demo: Storing and Retrieving Data Using Embeddings

  • Storing embeddings in a vector database.
  • Performing simple retrieval tasks using vector similarity search.
  • Practical example: Finding similar documents or sentences.

4.3 Practical Use Cases

• 4.3.1 Implementing a Simple Search Engine Using Embeddings and Vector Databases

  • Building a search engine that leverages embeddings for similarity-based searches.
  • End-to-end implementation: Data preprocessing, embedding generation, storing, and searching.

• 4.3.2 Demo: Scaling Up the Search with More Complex Queries and Datasets

  • Handling larger datasets and optimizing for performance.
  • Demonstrating complex search queries: Multi-vector searches, filtering, and ranking.
  • Real-world use cases: Semantic search, recommendation systems, and clustering.

• 4.3.2 Document Embedding and searching

  • querying the data in document as context.

4.4 Q&A and Wrap-up

• 4.4.1 Open Floor for Questions, Clarifications, and Discussions:

  • Addressing participant queries and providing additional insights.
  • Discussing challenges and best practices when working with embeddings and vector databases.

• 4.4.2 Summary of Key Takeaways and Additional Resources:

  • Recap of key concepts covered in the session.
  • Providing resources for further learning (articles, tutorials, documentation).

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Session 5: Retrieval-Augmented Generation (RAG)

Duration: 5-6 hours

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5.1 Introduction to RAG

• 5.1.1 Overview of Retrieval-Augmented Generation (RAG):

  • What is RAG, and why use it?
  • Key components of RAG: Knowledge retrieval and generation.
  • Applications of RAG in real-world scenarios (question answering, document retrieval, personalized chatbots).

• 5.1.2 Hands-On Activity: Building a Basic RAG System Using Python (30-45 minutes)

  • Step-by-step guide to implementing a simple RAG system.
  • Using Python libraries and frameworks (e.g., PyTorch, Hugging Face Transformers) to create a basic RAG model.
  • Integrating a pre-trained language model with a knowledge retrieval module.

• 5.1.3 Demo: Integrating Embeddings and Vector Databases for Knowledge Retrieval (15 minutes)

  • Demonstrating how embeddings and vector databases (e.g., Pinecone, FAISS) are used in the retrieval component of a RAG system.
  • Practical example: Retrieving relevant documents or context for a given query.

5.2 Data Preparation for RAG

• 5.2.1 Importance of Data Preparation in RAG:

  • Why data preparation is crucial for effective RAG systems.
  • Types of data needed for RAG: Structured vs. unstructured data.
  • Key considerations: Data quality, relevance, and diversity.

• 5.2.2 Hands-On Activity: Preparing and Cleaning Data for Embedding and Storage

  • Techniques for data cleaning and preprocessing (e.g., removing noise, normalizing text).
  • Preparing data for embedding generation: Tokenization, formatting, and embedding techniques.
  • Storing data in vector databases for fast retrieval.

• 5.2.3 Demo: Optimizing Data Storage for Fast Retrieval

  • Demonstration of techniques for optimizing data storage in vector databases.
  • Indexing strategies for faster search and retrieval.
  • Practical example: Setting up a highly performant retrieval system.

5.3 Advanced RAG Scenarios

• 5.3.1 Overview of Advanced RAG Scenarios:

  • Different use cases and scenarios for advanced RAG applications.
  • Examples: Document retrieval, personalized response generation, domain-specific knowledge augmentation.

• 5.3.2 Hands-On Activity: Implementing Different RAG Scenarios

  • Building RAG systems for specific use cases:
    • Document retrieval: Finding and generating summaries from large datasets.
    • Personalized responses: Tailoring responses based on user profiles or preferences.
  • Configuring retrieval modules and generation models for specific tasks.

• 5.3.3 Demo: Fine-Tuning RAG Models for Specific Tasks

  • Demonstrating how to fine-tune a RAG model for a particular task or domain.
  • Using domain-specific data to improve retrieval and generation performance.
  • Practical example: Fine-tuning a RAG model for customer support or medical consultation.

5.4 Q&A and Wrap-up

• 5.4.1 Open Floor for Questions, Clarifications, and Discussions:

  • Addressing participant queries and providing additional insights.
  • Discussing challenges and best practices when working with RAG systems.

• 5.4.2 Summary of Key Takeaways and Additional Resources:

  • Recap of key concepts covered in the session.
  • Providing resources for further learning (articles, tutorials, documentation).

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Session 6: Agents, Workflows, and Automation

Duration: 5-6 hours

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6.1 Introduction to Agents

• 6.1.1 Overview of Agents:

  • What are agents in the context of LangChain and AI-driven systems?
  • Types of agents: Task-oriented, conversational, and reactive agents.
  • Use cases: Automating tasks, decision-making, and handling complex workflows.

• 6.1.2 Hands-On Activity: Building Simple Agents Using LangChain

  • Step-by-step guide to creating a simple agent using the LangChain library.
  • Defining agent behaviors: How agents perceive inputs and make decisions.
  • Practical example: Building an agent to automate simple tasks like scheduling or data retrieval.

• 6.1.3 Demo: Integrating Agents with External APIs for Task Automation

  • Demonstration of integrating agents with external APIs (e.g., weather data, email services).
  • Practical example: Automating email notifications based on data changes or external triggers.

6.2 Workflow Automation

• 6.2.1 Overview of Workflow Automation:

  • What is workflow automation, and why is it important?
  • Key components: Triggers, actions, conditions, and agents.
  • Use cases: Streamlining business processes, reducing manual intervention, and improving efficiency.

• 6.2.2 Hands-On Activity: Designing and Implementing Workflows Using Python

  • Building a workflow from scratch using Python and LangChain.
  • Practical examples: Automating repetitive tasks, such as data processing or report generation.
  • Incorporating agents to handle decision-making within workflows.

• 6.2.3 Demo: Automating Complex Tasks with Agents and Chains

  • Demonstration of automating a complex, multi-step task using agents and chains.
  • Real-world example: Automating a data pipeline or customer onboarding process.

6.3 Advanced Automation Techniques

• 6.3.1 Advanced Techniques in Automation:

  • Overview of advanced automation methods: Error handling, logging, and monitoring.
  • Best practices for robust and scalable automation.

• 6.3.2 Hands-On Activity: Implementing Error Handling, Logging, and Monitoring in Workflows

  • Techniques for adding error handling and logging to workflows.
  • Implementing monitoring tools to track workflow performance and detect issues.
  • Practical example: Creating a robust automation system that handles failures gracefully.

• 6.3.3 Demo: Deploying a Fully Automated System with Real-World Applications

  • Demonstrating the deployment of an end-to-end automated system.
  • Real-world application examples: Customer service bots, automated reporting systems, and AI-driven decision-making.

6.4 Q&A and Wrap-up

• 6.4.1 Open Floor for Questions, Clarifications, and Discussions:

  • Addressing participant queries and providing additional insights.
  • Discussing challenges and best practices in building agents, workflows, and automation.

• 6.4.2 Summary of Key Takeaways and Additional Resources:

  • Recap of key concepts covered in the session.
  • Providing resources for further learning (articles, tutorials, documentation).

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