Mathematical Foundations of Machine Learning is a course in arithmetic and linear algebra with a focus on data science and machine learning, published by Udemi Academy. Mathematics and its sub-disciplines such as algebra and calculus are in fact the core and foundation of new knowledge such as artificial intelligence, data building science, and machine learning and deep and play a very important role in implementing systems based on these sciences.
he does. Learning the basics of math can help you gain a deeper understanding of machine learning issues and pave the way for your future career.
With high-level libraries and frameworks such as Scikit-learn and Keras, people with any level of knowledge can enter the world of science. But this does not mean that they specialize in these fields.
In order to deeply understand the logic behind the various algorithms and behind the scenes, systems based on mathematical machine learning will play a very important role and will open a window of infinity to you. One of the most important advantages of mastering mathematics is to identify bugs in the process of modeling and develop more efficient and lighter algorithms.
During the training process of the course and after each section, you will encounter a series of purposeful exercises, examples of Python application codes and tests, which play a very important role in developing your skills.
What you will learn in the Mathematical Foundations of Machine Learning course:
- Familiarity with the basics of linear algebra and arithmetic
- Work with Python-based libraries NumPy, TensorFlow, and PyTorch
- Implement calculations and operations and vector matrices necessary in machine learning and data science
- Reduce the multidimensionality of complex data and reduce it to essential data and elements with specific values and specific vectors, single value analysis method or SVD, and principal component analysis or PCA
- Solve unfamiliar and undefined variables using simple and advanced techniques
- Understand advanced differentiation rules such as the chain rule
- Deep understanding of machine learning algorithms
Instructor: Dr. Jon Krohn and Ligency I Team
Education Level: Basic to Advanced
Number of Courses: 105
Training Duration: 15 hours and 33 minutes
Course topics on 2021/11
Prerequisites for Mathematical Foundations of Machine Learning
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.
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