Download Udemy – Mathematical Foundations of Machine Learning 2023-12

Mathematical Foundations of Machine Learning


Mathematical Foundations of Machine Learning is a calculus and linear algebra training course focused on data science and machine learning, published by Yudemy Academy. Mathematics and its sub-branches such as algebra and calculus are actually the main core and foundation of new knowledge such as artificial intelligence, data structure science and deep machine learning and play a very important role in the implementation of systems based on these sciences. he does. Learning the basic principles of mathematics can help you deeply understand machine learning issues and pave the way for your future career. With high-level libraries and frameworks such as Scikit-learn and Keras, people of any knowledge level can enter the world of data science. But this does not mean their expertise in these fields.

In order to deeply understand the logic behind different algorithms and behind the scenes of systems based on machine learning, mathematics plays a very important role and will open a window of infinity to you. One of the most important advantages of mastering mathematics is the identification of bugs in the modeling process and the development of more optimal and lighter algorithms. During the educational process of the course and after each section, you will encounter a series of targeted exercises, examples of Python application codes, and tests that play a very important role in expanding your skills.

What you will learn in the Mathematical Foundations of Machine Learning course:

  • Getting to know the basic principles of linear algebra and calculus
  • Working with Python-based libraries NumPy, TensorFlow and PyTorch
  • Implementation of essential vector and matrix calculations and operations in machine learning and data science
  • Reducing the multiple dimensions of complex data and reducing it to essential data and elements with special value and special vector, single value analysis method or SVD and principal component analysis or PCA
  • Solving unknown and undefined variables using simple and advanced techniques
  • Understanding advanced differentiation rules such as the chain rule
  • Deep understanding of machine learning algorithms

Course details

Publisher: Yudmi
teacher: Dr. Jon Krohn , Agency I Team , SuperDataScience Team
English language
Training level: introductory to advanced
Number of courses: 114
Training duration: 16 hours and 26 minutes

Course headings

Prerequisites for the Mathematical Foundations of Machine Learning course

All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.

Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.

Course images

Mathematical Foundations of Machine Learning

Introduction video of the Mathematical Foundations of Machine Learning course

Installation guide

After Extract, view with your favorite Player.

English subtitle

Quality: 720p


The version of 2022/7 compared to 2021/9 has increased the number of 8 lessons and the duration of 52 minutes. Also, the course quality has been increased from 720p to 1080p.

The version of 2023/12 compared to 2022/7 has increased by 1 lesson and duration of 1 minute. Also, the quality of the course has been reduced from 1080p to 720p.

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Download part 1 – 1 GB

Download part 2 – 1 GB

Download part 3 – 1 GB

Download part 4 – 1 GB

Download part 5 – 686 MB

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