Machine Learning for Absolute Beginners - Level 1

Machine Learning for Absolute Beginners - Level 1

by Idan Gabrieli
Released December 2020
Publisher(s): Packt Publishing
ISBN: 9781801074780

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Video description

Embark on a journey into Machine Learning designed for absolute beginners. Start with the rise of Artificial Intelligence, exploring its evolution and key concepts like classical programming versus ML, deep learning, and applied versus generalized AI. Gain insight into why AI has become so impactful in today's world.

Next, dive into the classifications of Machine Learning systems, including supervised, unsupervised, and reinforcement learning. Concepts like regression, clustering, and dimensionality reduction are explained simply, with practical examples to make them accessible. You'll also learn how ML models are trained, aiming for generalization and real-world applications.

Finally, discover the power of generative AI and its transformative potential. From neural networks and large language models to common challenges like bias and security, you'll explore both its opportunities and limitations. By the end, you'll have a solid understanding of Machine Learning and how it applies to the evolving AI landscape.

To access the supplementary materials, scroll down to the 'Resources' section above the 'Course Outline' and click 'Supplemental Content.' This will either initiate a download or redirect you to GitHub.

What you will learn

  • Explain the basics of AI, ML, and Deep Learning concepts.
  • Identify key Machine Learning system classifications.
  • Understand how ML models are trained and generalized.
  • Explore neural networks and their applications.
  • Recognize challenges like bias and prompt sensitivity in AI.
  • Apply ML concepts to real-world AI use cases effectively.

Audience

This course is ideal for absolute beginners eager to understand Machine Learning and AI. No prior knowledge of programming or data science is required, making it accessible to those with a curiosity for technology and its impact.

About the Author

Idan Gabrieli: Idan Gabrieli has spent the past decade working in engineering roles at the forefront of Israel's high-tech industry, widely known as the start-up nation. With extensive experience collaborating with hundreds of companies, Idan has helped transform challenges into practical solutions, leveraging cutting-edge technologies like cloud computing, machine learning, data science, and electronics.

Since 2014, he has created and published online courses, educating thousands of students worldwide. Recognized as a top-rated instructor in 2021-2022, Idan is celebrated for simplifying complex topics into structured, engaging, and high-quality educational content with clear learning objectives tailored to diverse audiences.

Table of contents

  1. Chapter 1 : Getting Started with Level 1!
    1. Welcome!
  2. Chapter 2 : The Rise of Artificial Intelligence
    1. Artificial Intelligence (AI) - The Future
    2. Artificial Intelligence
    3. Classical Programming
    4. Machine Learning
    5. Deep Learning
    6. Applied vs. Generalized Artificial Intelligence (AI)
    7. Why Do We Need AI Today?
  3. Chapter 3 : Introduction to Machine Learning
    1. Machine Learning (ML) Terminology
    2. The Black Box Metaphor
    3. Features and Labels
    4. Training a Model
    5. Aiming for Generalization
  4. Chapter 4 : Classification of Machine Learning (ML) Systems
    1. The Degree of Supervision
    2. Supervised Learning
    3. Classification
    4. Regression
    5. Unsupervised Learning
    6. Clustering
    7. Dimension Reduction
    8. Reinforcement Learning
    9. Decision-Making Agent
  5. Chapter 5 : The Magic Behind Generative AI
    1. Introduction
    2. Artificial Neural Networks
    3. Deep Learning Architectures
    4. Foundation Models
    5. Large Language Models (LLMs)
    6. Model Types
    7. Prompt and Tokens
    8. Total Tokens and Context Window
    9. Next Token Please!
    10. Self-Supervised Learning
    11. Improving and Adapting LLMs
    12. Summary
  6. Chapter 6 : Key Challenges and Limitations
    1. Introduction
    2. Prompt Sensitivity
    3. Knowledge Cutoff
    4. It is not Deterministic
    5. Structured Data
    6. Hallucinations
    7. Lack of Common Sense
    8. Bias and Fairness
    9. Data Privacy, Security, and Misuse
    10. Summary
  7. Chapter 7 : Unleash the Power of Generative AI
    1. Introduction
    2. Text-Image-Video-Audio Generation
    3. Web-Based vs Application-Based (APIs)
    4. Use Case - Brainstorm Assistant
    5. Use Case - Summarization
    6. Use Case – Text Enhancement
    7. Use Case - Code Generation
    8. Use Case – Content as a Framework
    9. Use Case – Images on Demand
    10. Use Case – Boosting AI-Based Apps
    11. Best Practices for Prompts
    12. Summary
  8. Chapter 8 : Course Summary
    1. Let's Recap and Thank You!

Product information

  • Title: Machine Learning for Absolute Beginners - Level 1
  • Author(s): Idan Gabrieli
  • Release date: December 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781801074780