Jumpstart Python & Gen AI: Zero to Hero for Beginners

This 16-lecture course is designed to supply a stable basis in Python programming and an introduction to Generative AI. Tailor-made for novices, the course contains each theoretical classes and hands-on tasks to make sure that learners can apply their data in real-world situations. The complete course is extra of a narrative telling format to novices in realtime. The recordings can provide you an immersive expertise at school.

Lecture 1: Introduction to Generative AI and Python

• Overview of the course construction and targets.
• Introduction to Python and its significance in AI.
• Overview of Generative AI, together with its purposes and relevance in in the present day’s world.

Python Fundamentals (Lectures 2–10)

Lecture 2: Introduction to Python Fundamentals

• Overview of programming and Python as a language.
• Organising and utilizing Google Colab for coding.
• Exploring GitHub for code storage and collaboration.
• Primary syntax in Python: print statements, feedback.

Lecture 3: Variables and Information Varieties

• Understanding variables and their position in programming.
• Exploring completely different information varieties: integers, floats, strings.
• Easy enter and output operations utilizing enter() and print() features.

Lecture 4: Management Constructions

• Conditional statements: if, elif, else.
• Comparability and logical operators.
• Introduction to loops: whereas loops and their use in repetitive duties.

Lecture 5: Lists and For Loops

• Lists: creation, indexing, slicing, and fundamental record strategies.
• Introduction to for loops and their purposes in iterating by lists.

Lecture 6: Units and Loops

• Working with units: creation and strategies.
• Continuation of for loops, utilized to units and different information constructions.

Lecture 7: Tuples and Dictionaries

• Overview of tuples: creation and properties.
• Working with dictionaries: creation, accessing values, and fundamental dictionary strategies.

Lecture 8: Features in Python

• Understanding and utilizing built-in features.
• Defining customized features, parameters, and return values.

Lecture 9: Modules and Libraries

• Introduction to Python modules and libraries.
• Utilizing the maths module and understanding Python packages.
• Introduction to PIP for managing Python libraries.

Lecture 10: String Operations and File Dealing with

• String operations and formatting.
• Studying from and writing to information utilizing Google Colab’s file system.
• Fingers-on undertaking: Create a easy Python undertaking to display understanding of Python fundamentals.

Introduction to Generative AI (Lectures 11–13)

Lecture 11-12: Textual content Era and LLMs

• Overview of textual content technology instruments and Massive Language Fashions (LLMs) like ChatGPT, Gemini, and Claude.
• Fingers-on workouts utilizing OpenAI Playground and Google AI Studio for textual content technology.
• Sensible comparability of outputs from completely different AI instruments.

Lecture 13: AI-driven Code Era and Immediate Engineering

• Introduction to AI-based code technology utilizing instruments like ChatGPT and Claude.
• Understanding Cursor IDE for AI-assisted coding.
• Sensible undertaking: Construct a easy net web page utilizing AI-generated code.

Superior Generative AI Ideas (Lectures 14–16)

Lecture 14: Picture Era and Working LLMs Regionally

• Overview of picture technology instruments resembling DALL-E, Midjourney, and Steady Diffusion.
• Sensible train: Producing and animating photographs utilizing runwayML.
• Working open-source LLMs domestically utilizing instruments like Ollama and LMStudio.

Lecture 15: Retrieval Augmented Era (RAG)

• Utilizing LLMs with customized information by RAG strategies.
• Introduction to embeddings and vector shops (chromaDB, qdrant).
• Sensible train: Constructing a RAG pipeline to course of and retailer PDFs in qdrant cloud.

Lecture 16: Constructing Actual AI Tasks

• Introduction to Langchain and LlamaIndex.
• Fingers-on undertaking: Create a RAG-based question-answering system on a webpage.
• Exploring the open-source AI ecosystem and subsequent steps for continued studying.

By the tip of the course, learners could have gained a radical understanding of Python programming and sensible expertise with Generative AI, enabling them to construct AI-driven tasks.