Summary, Description
The professor of this course is an AI specialist and he has found that without knowing the main concepts in mathematics and statistics, many students and young professionals make the mistake of studying machine learning. This course would help in a major way to fill the void.
Because machine learning is an area at the intersection of several disciplines, such as analytics, probability, computer science, and mathematics, assimilating these key concepts is important for practitioners and budding enthusiasts.
Ses insights will help you lay a solid base for creating a flourishing artificial intelligence career.
This course teaches you the principles, but from an implementation viewpoint, in mathematics and statistics. One thing is to hear about the concepts, but knowing the implementation of those concepts is another matter. Deploying and using machine learning will still remain difficult without this awareness.
You can study topics such as central propensity vs dispersion measurements, checking of theories, population vs survey, outliers and many fascinating concepts. Behind multiple algorithms, you can also gain insights into decent gradients and mathematics.
In this course, we discuss the topics below:
Central Inclination vs Dispersion steps
Mean vs Standard Deviation
File Styles
Related vs autonomous variables
The probability
Population for Sample Vs
Testing Hypothesis
Stability Definition
Distribution styles
From Outliers
Mathematics behind algorithms in machine learning such as regression, decision tree and kNNN
Descent by gradient.
For whom this course is intended:
Computer scientists, Python engineers, ML Experts, data science team management IT administrators
