explanation
Mathematical Foundations of Machine Learning is an arithmetic and linear algebra course with a focus on data science and machine learning published by Udemy Academy. Subdisciplines such as mathematics, algebra, and calculus are actually the core and foundation of new knowledge such as artificial intelligence, data construction science, and machine learning, and play a very important role in implementing systems based on these sciences. He is. Learning the basics of mathematics can help you understand machine learning problems more deeply and pave the way for your future career. Advanced libraries and frameworks such as Scikit-learn and Keras allow people of all levels of knowledge to enter the world of science. However, this does not mean that they specialize in that field.
In order to deeply understand various algorithms and the logic behind them, systems based on machine learning mathematics play a very important role and will open infinite windows for you. One of the most important benefits of mastering math is identifying bugs in the process of modeling and developing more efficient and lightweight algorithms. At the end of the course’s curriculum and each section, you will be exposed to a series of purposeful exercises, examples of Python application code, and tests, which are invaluable in developing your skills.
What you will learn from the mathematical foundations of machine learning
- Knowledge of the basics of linear algebra and calculus
- Work with Python-based libraries NumPy, TensorFlow, and PyTorch
- Implements calculations, operations, and vector matrices required for machine learning and data science.
- Methods for reducing the multidimensionality of complex data into essential data and elements with specific values and specific vectors, Single Value Analysis (SVD) and Principal Component Analysis (PCA) methods.
- Solve unfamiliar and undefined variables using simple and advanced techniques.
- Understanding advanced differential laws such as chain rule
- Deep understanding of machine learning algorithms
Course specifications
publisher: Udemy
Instructor: Dr. Jon Krohn, Ligency I Team, SuperDataScience Team
Language:English
Level: Beginner~Advanced
Number of classes: 114
Duration: 16 hours 26 minutes
course topic
Mathematical Foundations of Machine Learning Prerequisites
All code demos are in Python, so having experience with Python or another object-oriented programming language will help you follow along with the hands-on examples.
Once you become familiar with middle school level math, the lessons will be easier to follow. If you’re good at dealing with quantitative information, such as understanding charts and rearranging simple equations, you should be well prepared to keep up with all the math.
movie
Video introducing the mathematical basics of machine learning
installation manual
After extracting, watch with your favorite players.
Subtitles: English
Quality: 720p
Changes:
The 2022/7 version has been increased to 8 lessons and 52 minutes in length compared to the 2021/9 version. Additionally, course quality has been improved from 720p to 1080p.
The 2023/12 version has increased number of lessons and 1 minute length compared to the 2022/7 version. Additionally, the course quality has been lowered from 1080p to 720p.
download link
File password: www.downloadly.ir
size
4.68GB