Complete Mathematical Intro to Machine Learning [2025]

Complete Mathematical Intro to Machine Learning [2025] free download

Learn the essentials of Machine Learning, including Theory, Coding, and Mathematics | Taught by Univesity Instructors

Are you ready to build a strong and practical foundation of machine learning?

This comprehensive course is designed to take you from the foundational principles of machine learning to advanced techniques in regression, classification, clustering, and neural networks. Whether you're a student, a data science enthusiast, or a professional looking to sharpen your skills, this course will give you the tools and intuition you need to work effectively with real-world data.

What You'll Learn In This Course:

We begin with a conceptual overview of machine learning, exploring different types of learning paradigms—supervised, unsupervised, and more. You’ll learn how to approach problems, evaluate models, and understand common pitfalls such as overfitting, bad data, and inappropriate assumptions.

From there, we dive into Modelling:

• Regression

• Linear Models

• Regularization (Ridge, LASSO)

• Cross-Validation

• Flexible Approaches like Splines and Generalized Additive Models

Classification Techniques are covered in depth, including:

• Logistic Regression

• KNN, Generative Models

• Decision Trees

• Neural Networks and Backpropagation for more Advanced Modeling.

Finally, we explore Clustering:

• K-Means Clustering

• Hierarchical Methods

• Discussing Algorithmic Strengths, Challenges, and Evaluation Techniques.

  
  

Practice with Hands-on Examples:

We teach the concepts of Machine Learning with engaging, hands-on examples using well known datasets such as Gapminder and Palmer Penguins. Mathematical formulas are broken down and explained thoroughly step by step. Not only will you gain the theoretical understanding of Machine Learning, but also the practical intuition and experience for future projects.

With real-world datasets, detailed derivations, and clear explanations, this course bridges the gap between theory and application. By the end of the course, you will have a strong arsenal of fundamential machine learning techniques, know when and how to apply them, and understand the mathematical theories that power them—all with practical, real-world relevance.