Udemy

Supervised Machine Learning From First Principles

Enroll Now
  • 8,998 Students
  • Updated 7/2024
4.1
(25 Ratings)
CTgoodjobs selects quality courses to enhance professionals' competitiveness. By purchasing courses through links on our site, we may receive an affiliate commission.

Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
20 Hour(s) 6 Minute(s)
Language
English
Taught by
Houston Muzamhindo
Rating
4.1
(25 Ratings)
3 views

Course Overview

Supervised Machine Learning From First Principles

Discussing the principles behind the most used Machine Learning algorithms

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.

Course Content

  • 7 section(s)
  • 79 lecture(s)
  • Section 1 Introduction to Machine Learning
  • Section 2 Introduction to Statistical Learning
  • Section 3 Linear Regression
  • Section 4 Classification
  • Section 5 Validation and The Bootstrap Methods
  • Section 6 Linear Model Selection and Regularization
  • Section 7 Tree Based Methods

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

Reviews

  • S
    Shreya.R
    4.0

    Very helpful

  • K
    Karim Ennouri
    5.0

    Tres bon cours, je recommande vivement!

  • M
    Mohamed Ahmed Yousif Omer
    5.0

    because It is useful course

  • P
    Pramila Vimalraj
    3.0

    very theoritical

Start FollowingSee all

We use cookies to enhance your experience on our website. Please read and confirm your agreement to our Privacy Policy and Terms and Conditions before continue to browse our website.

Read and Agreed