Foundations of AI, LLMs, and Development Environments
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Foundations of AI, LLMs, and Development Environments
This course is part of AI & LLM Engineering Mastery - GenAI, RAG Complete Guide Specialization
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What you'll learn
Set up a development environment for AI and LLM development.
Master Python fundamentals and data structures to apply in AI projects.
Understand deep learning and machine learning, and how they relate to AI.
Build and enhance LLMs using popular libraries like Hugging Face's Transformers.
Skills you'll gain
Tools you'll learn
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There are 7 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain a comprehensive understanding of AI, Large Language Models (LLMs), and their development environments. You'll start by learning the foundational principles of AI and LLMs, followed by hands-on demonstrations of the projects you'll build, giving you practical experience. The course is structured progressively, from environment setup to deep dives into Python programming, culminating in the construction of LLMs using libraries like Hugging Face’s Transformers. As you work through each section, you'll be guided step-by-step with tutorials and practice exercises that reinforce key concepts. The journey includes setting up your development environment, mastering Python fundamentals, exploring deep learning and machine learning, and diving into the complexities of Generative AI. Key concepts such as the transformer architecture, self-attention mechanism, and using OpenAI APIs will be explored in detail. By completing each module, you will build your coding and problem-solving skills, progressively building toward more advanced techniques in AI development. This course is ideal for those wanting to break into AI and machine learning development. The target audience includes beginners with some basic understanding of programming, specifically those interested in AI applications. No prior experience in AI is required, though familiarity with Python will be beneficial. By the end of the course, you will be able to set up your development environment for AI projects, understand and implement LLMs using transformer architecture, create and deploy AI models, and integrate OpenAI’s models through API calls.
In this module, we will introduce the course’s objectives and lay out the structure that will guide you through mastering AI and LLM engineering. You'll gain an overview of the course journey, including a demo of what you’ll be building and strategies for getting the most out of your learning experience.
What's included
4 videos2 readings
4 videos•Total 17 minutes
- Introduction to the Specialization•5 minutes
- Demo: What You'll Build in This Course•8 minutes
- Course Structure•1 minute
- How to Get the Most from This Course•2 minutes
2 readings•Total 20 minutes
- Introduction to the Course 'Foundations of AI, LLMs, and Development Environments'•10 minutes
- Full Specialization Resources•10 minutes
In this module, we will guide you through setting up your development environment, including installing Python and Visual Studio Code, ensuring you’re ready for hands-on programming in the AI and LLM space. You will be equipped to start coding immediately with proper setup and configuration.
What's included
6 videos1 assignment
6 videos•Total 13 minutes
- Development Environment Setup: Overview•1 minute
- Install Python on Windows: For Windows Users•3 minutes
- Install Python on Mac: For Mac Users•2 minutes
- Download Visual Studio Code•1 minute
- Install the Python Extension Pack for VS Code•2 minutes
- Running First Python Program in VS Code•4 minutes
1 assignment•Total 15 minutes
- Development Environment Setup - Assessment•15 minutes
In this module, we will provide an in-depth exploration of Python, covering essential topics such as variables, data types, control structures, functions, and object-oriented programming. Whether you're new to Python or need a refresher, this section will ensure you're proficient in the language for AI development.
What's included
77 videos1 assignment
77 videos•Total 394 minutes
- What Is Python and Where It's Used?•3 minutes
- Python Compilation and Interpretation Process•3 minutes
- Declaring Variables in Python•5 minutes
- Data Types•5 minutes
- Python f-Strings•3 minutes
- Numbers: Integers and Floats•3 minutes
- Introduction to Lists: Accessing and Modifying Them•8 minutes
- f-Strings and Individual Values from a List•3 minutes
- Sorting a List and Getting a List Length•7 minutes
- Lists and Loops: Looping Through a List•4 minutes
- Making a List of Numbers with Loops and the Range Function•6 minutes
- Statistics Functions for Numbers•2 minutes
- Generate Even Numbers with the List and Range•3 minutes
- Important: Code Organization Note•1 minute
- List Comprehension•4 minutes
- Tuples•3 minutes
- Branching: If Statements and Booleans•7 minutes
- The Elif and the in Keywords•4 minutes
- Hands-On: Using and and or Logical Operators•6 minutes
- and or Logical Operators•1 minute
- Checking for Inequalities•2 minutes
- Hands-On: Inner If-Statements•4 minutes
- Data Structures: Dictionaries—Introduction and Declaring and Accessing Values•6 minutes
- Modifying a Dictionary•5 minutes
- Iterating Through a Dictionary•4 minutes
- Nested Dictionaries and Looping Through Them•7 minutes
- Looping Through a Dictionary with a List Inside•4 minutes
- User Input and While Loops: User Input—Introduction•7 minutes
- Hands-On: Odd or Even Number•5 minutes
- While Loops and Simple Quit Program•7 minutes
- Hands-On: Quiz Game•8 minutes
- Removing All Instances of Specific Values from a List•1 minute
- Hands-On: Dream Travel Itinerary Program—Filling a Dictionary with User Input•9 minutes
- Functions: Introduction•4 minutes
- Passing Information to a Function (Parameters)•4 minutes
- Positional and Named Arguments•2 minutes
- Default Values: Parameters•3 minutes
- Return Values from a Function•4 minutes
- Hands-On: Returning an Integer and Intro to DocString•5 minutes
- Functions: Passing a List as Argument•2 minutes
- Passing an Arbitrary Number of Arguments to a Function•6 minutes
- Introduction to Modules: Importing Specific Functions from a Module•7 minutes
- Using the "as" as an Alias•2 minutes
- Classes and OOP: Object-Oriented Programming—The init and str Methods•10 minutes
- The init and str Methods•9 minutes
- Adding More Methods to the Class•2 minutes
- Setting a Default Value for an Attribute•2 minutes
- Modifying Class Attribute: Directly and with Methods•3 minutes
- Inheritance: Create an Ebook—Child Class•11 minutes
- Overriding Methods•6 minutes
- Creating and Importing from a Module•5 minutes
- The Object Class: Overview•8 minutes
- The Python Standard Library•2 minutes
- Random Module: Random Fruit Hands-On•7 minutes
- Hands-On: Random Fruit with Choice Module Method•4 minutes
- Using Datetime Module•6 minutes
- Writing and Reading Files: Do Useful Tasks with Python•1 minute
- The Path Class and Reading a Text File•5 minutes
- Resolving Path: Reading from a Subdirectory with Path•2 minutes
- Path Properties Overview•3 minutes
- Writing to Text File with Path•3 minutes
- Read and Write to File Using the "with" Keyword•4 minutes
- Handling Exceptions•6 minutes
- The "FileNotFound" and "IndexError" Exception Types•4 minutes
- Custom Exception Creation and Handling•8 minutes
- JSON: Reading and Writing to a JSON File•7 minutes
- Hands-On: Writing and Reading Countries to JSON File•9 minutes
- Hands-On: File Organizer•13 minutes
- Python Virtual Environment and PIP•4 minutes
- Setting Up Virtual Environment and Installing a Package•7 minutes
- Hands-On: Watermarker Python Tool•1 minute
- Building an Image Watermarker in Python: Part 1•17 minutes
- Generating the Watermarked Images•11 minutes
- Reading CSV File: Introduction•6 minutes
- Getting the CSV Header Position•5 minutes
- Reading Data from a CSV Column•4 minutes
- Plotting a Graph with CSV Data•12 minutes
1 assignment•Total 15 minutes
- Optional: Python Deep Dive—Master Python Fundamentals - Assessment•15 minutes
In this module, we will take a comprehensive look at deep learning and machine learning, breaking down their core components and how they relate to AI. You’ll gain clarity on the essential elements of neural networks and how they differ from traditional machine learning techniques.
What's included
10 videos1 assignment
10 videos•Total 61 minutes
- Deep and Machine Learning Deep Dive: Overview and Breakdown•9 minutes
- Deep Learning Key Aspects•11 minutes
- Deep Neural Network Dissection: Full Dive with Analogies•9 minutes
- The Single Neuron Computation: Deep Dive•6 minutes
- Weights: Deep Dive•3 minutes
- Activation Functions: Deep Dive with Analogies•6 minutes
- Deep Learning Summary•2 minutes
- Machine Learning Introduction: Machine Learning vs. Deep Learning•5 minutes
- Learning Types: Education System Analogy•6 minutes
- Comparative Capabilities: Deep Learning, Machine Learning, and AI Summary•4 minutes
1 assignment•Total 15 minutes
- Understanding Deep and Machine Learning - Assessment•15 minutes
In this module, we will delve into the world of Generative AI, discussing its architecture, key technologies, and the challenges it faces. By the end, you’ll have a solid understanding of how GenAI systems function and the components that make them work.
What's included
3 videos1 assignment
3 videos•Total 12 minutes
- GenAI Introduction and Architecture Overview•5 minutes
- GenAI Key Technologies: Limitations and Challenges•6 minutes
- GenAI Key Components Overview and Summary•2 minutes
1 assignment•Total 15 minutes
- Generative AI: Architecture and Core Technologies - Assessment•15 minutes
In this module, we will introduce you to Large Language Models (LLMs), including the transformer architecture and the self-attention mechanism. You’ll also engage in hands-on development, building and enhancing your own LLM with the popular Hugging Face library.
What's included
7 videos1 assignment
7 videos•Total 49 minutes
- LLMs: Overview•7 minutes
- The Transformer Architecture: Fundamentals•8 minutes
- The Self-Attention Mechanism: Analogy•5 minutes
- The Transformers Library: Deep Dive•4 minutes
- Hands-On: Create a Simple LLM from the Transformers Library•10 minutes
- Hands-On: Enhanced Transformers LLM•10 minutes
- Open-Source vs. Closed-Source Models: Overview•5 minutes
1 assignment•Total 15 minutes
- LLMs: Concepts, Architecture, and Hands-On Development - Assessment•15 minutes
In this module, we will walk you through setting up an OpenAI account, generating your API key, and using the API to make your first call to an OpenAI model. You'll gain practical skills in integrating OpenAI’s models into your own AI applications.
What's included
3 videos1 reading3 assignments
3 videos•Total 16 minutes
- Set Up OpenAI Account and API Key•5 minutes
- Using APIs Effectively in AI Projects•3 minutes
- Hands-On: Making Our First Call to OpenAI Model•7 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Foundations of AI, LLMs, and Development Environments'•10 minutes
3 assignments•Total 90 minutes
- OpenAI Models and Setup - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
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Frequently asked questions
Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It includes learning, reasoning, and self-correction. AI is relevant because it powers a wide range of technologies that improve efficiency, decision-making, and even creativity in fields such as healthcare, finance, and robotics. As AI continues to evolve, it holds the potential to revolutionize how industries operate, making this an essential area to understand for anyone interested in technology.
The "Foundations of AI, LLMs, and Development Environments" course covers the foundational principles and practical applications of Artificial Intelligence (AI), Large Language Models (LLMs), and the necessary development environments to work with these technologies. The course explores core topics, including Python programming, deep learning, machine learning, generative AI, and hands-on development using tools like the Hugging Face Transformers library. You will also learn to set up essential software environments and gain practical skills for building AI applications.
Upon completing this course, you'll have the skills to set up development environments for AI and LLM projects, build and enhance AI applications, and work with large language models. You’ll also be proficient in Python programming, data manipulation, machine learning, and deep learning concepts. Additionally, you'll understand how to interact with APIs, especially those provided by OpenAI, to integrate advanced language models into your projects.
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