Grasp Python and Dive into Generative AI with No Prior Expertise: Study to Code and Create Utilizing Actual-World Instruments
What you’ll study
Newcomers who dont code of their whole life time
People who find themselves in non tech function keen to search for technical alternatives
Individuals who have eager curiosity on studying Gen AI
Perceive how Gen AI trade works by creating actual time purposes
Why take this course?
This 16-lecture course is designed to supply a stable basis in Python programming and an introduction to Generative AI. Tailor-made for inexperienced persons, the course contains each theoretical classes and hands-on tasks to make sure that learners can apply their information in real-world situations. Your entire course is extra of a narrative telling format to inexperienced persons in realtime. The recordings may give you an immersive expertise in school.
Lecture 1: Introduction to Generative AI and Python
- Overview of the course construction and aims.
- Introduction to Python and its significance in AI.
- Overview of Generative AI, together with its purposes and relevance in immediately’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.
- Fundamental syntax in Python: print statements, feedback.
Lecture 3: Variables and Knowledge Sorts
- Understanding variables and their function in programming.
- Exploring completely different knowledge 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 way of lists.
Lecture 6: Units and Loops
- Working with units: creation and strategies.
- Continuation of for loops, utilized to units and different knowledge buildings.
Lecture 7: Tuples and Dictionaries
- Overview of tuples: creation and properties.
- Working with dictionaries: creation, accessing values, and fundamental dictionary strategies.
Lecture 8: Capabilities 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 mathematics 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 recordsdata utilizing Google Colab’s file system.
- Fingers-on venture: Create a easy Python venture to show understanding of Python fundamentals.
Introduction to Generative AI (Lectures 11–13)
Lecture 11-12: Textual content Technology and LLMs
- Overview of textual content era instruments and Massive Language Fashions (LLMs) like ChatGPT, Gemini, and Claude.
- Fingers-on workout routines utilizing OpenAI Playground and Google AI Studio for textual content era.
- Sensible comparability of outputs from completely different AI instruments.
Lecture 13: AI-driven Code Technology and Immediate Engineering
- Introduction to AI-based code era utilizing instruments like ChatGPT and Claude.
- Understanding Cursor IDE for AI-assisted coding.
- Sensible venture: Construct a easy internet web page utilizing AI-generated code.
Superior Generative AI Ideas (Lectures 14–16)
Lecture 14: Picture Technology and Operating LLMs Domestically
- Overview of picture era instruments akin to DALL-E, Midjourney, and Secure Diffusion.
- Sensible train: Producing and animating photographs utilizing runwayML.
- Operating open-source LLMs domestically utilizing instruments like Ollama and LMStudio.
Lecture 15: Retrieval Augmented Technology (RAG)
- Utilizing LLMs with customized knowledge by way of 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 Initiatives
- Introduction to Langchain and LlamaIndex.
- Fingers-on venture: Create a RAG-based question-answering system on a webpage.
- Exploring the open-source AI ecosystem and subsequent steps for continued studying.
By the top of the course, learners may have gained a radical understanding of Python programming and sensible expertise with Generative AI, enabling them to construct AI-driven tasks.
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