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Supervised Machine Learning From First Principles
Rating: 4.1 out of 5(25 ratings)
8,998 students

Supervised Machine Learning From First Principles

Discussing the principles behind the most used Machine Learning algorithms
Created byHouston Muzamhindo
Last updated 7/2024
English

What you'll learn

  • Machine Learning Principles
  • The principles behind Machine Learning algorithms (not just the codes!)
  • Regression (Linear Regression, Multiple Linear Regression, Polynomial Regression, and Support Vector Regression)
  • Classification (Logistic Regression, k-Nearest Neighbours, Trees, and Support Vector Machines)
  • Other principles such as Cross Validation, AIC, BIC, and choosing the right metrics for your algorithm

Course content

7 sections • 79 lectures • 20h 6m total length

  • Introduction7:37

    More often that not, when people start studying Machine Learning, they rush into coding. In this course, we are going to start Machine Learning from *first principles*. In this video, I discuss the topics that will be covered in this course.

Requirements

  • An interest in knowing machine learning from first principles without jumping straight into coding

Description

Machine Learning Principles: Unlocking the Power of Algorithms and Concepts

Are you ready to take your Machine Learning skills to the next level? This course is designed to introduce you to the fundamental principles behind Machine Learning algorithms and concepts, empowering you to become a more effective and insightful practitioner in this rapidly evolving field.

Why This Course?

Machine Learning is more than just a tool – it's a powerful approach to problem-solving that requires a deep understanding of its underlying principles. Without this foundation, you may find yourself:

  • Struggling to interpret model results effectively

  • Unsure why one model outperforms another

  • Unable to choose the most appropriate metrics for your specific problems

  • Limited in your ability to innovate and create custom solutions

This course aims to bridge the gap between simply using Machine Learning tools and truly mastering the science behind them.

What You'll Learn

Throughout this course, you'll gain invaluable insights into:

  1. The core mathematical and statistical concepts driving Machine Learning algorithms

  2. How to interpret common evaluation metrics (e.g., MSE, accuracy, precision, recall) and understand their real-world implications

  3. The strengths and weaknesses of various Machine Learning models and when to apply them

  4. Techniques for feature selection, preprocessing, and model optimization

  5. The ethical considerations and potential biases in Machine Learning applications

Course Structure

We'll cover a range of topics, including but not limited to:

  • Regression

  • Classification

  • Resampling Methods

  • Bootstrap

  • Ensembles

  • SVMs

Each section includes Python code discussions with suggested homework to reinforce your learning and help you apply these principles to actual problems.

Who Should Take This Course?

This course is ideal for:

  • Data scientists looking to deepen their theoretical knowledge

  • Software engineers transitioning into Machine Learning roles

  • Students pursuing careers in AI and data analysis

  • Professionals seeking to leverage Machine Learning in their industry

Whether you're just starting your journey in Machine Learning or looking to solidify your understanding, this course will provide you with the insights and skills needed to excel in this exciting field.

Who this course is for:

  • Beginners who are curious to start their understanding of Machine Learning without jumping head-first into the codes

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