Udemy - Mathematical Introduction to Machine Learning

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Free Download Udemy - Mathematical Introduction to Machine Learning
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 11h 15m | Size: 13.2 GB
A mathematical journey through common machine learning frameworks in regression, classification, and clustering.

What you'll learn
Learn basics of machine learning, including both supervised learning and unsupervised learning.
Grasp the mathematical foundations of the most common machine learning framework.
Be able to differentiate appropriate machine learning models for specific use cases (e.g. regression vs. classification vs. clustering).
Have a well-tailored toolbox of machine learning algorithms to apply to data science problems.
Be familiar with how to fit machine learning models in R and Python.
Be familiar with the challenges ones can face in machine learning.
Requirements
Linear Algebra
Probability
Statistics
Multivariate Differential Calculus
Beginner experience in R
Beginner experience in Python
Description
Are you ready to gain a deep and practical understanding 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.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 regression, covering linear models, regularization (Ridge, LASSO), cross-validation, and flexible approaches like splines and Generalized Additive Models-all illustrated with hands-on examples using datasets like Gapminder and Palmer Penguins.Classification techniques are covered in depth, including logistic regression, KNN, generative models, and decision trees, along with neural networks and backpropagation for more advanced modeling.Finally, we explore clustering, from k-means to hierarchical methods, discussing algorithmic strengths, challenges, and evaluation techniques.With real-world datasets, detailed derivations, and clear explanations, this course bridges the gap between theory and application.
Who this course is for
Future machine learning engineers or data scientists looking to deeply understand machine learning.
Mathematically curious individuals.
Homepage

https://www.udemy.com/course/intro-machine-learning/

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