Data science is a technical science, and like any technical subject, a mathematical language is used to formulate ideas. More and more solutions are trying to automate the entire machine learning process, but if a person does not understand the mathematical formalism that underlies the algorithms, it is impossible to test and debug models that can lead to false conclusions.
In this course, students explore the concepts of linear algebra, probability theory, and statistics, which are key to exploratory data analysis and understanding and developing machine learning algorithms.
Although we are located in Ukraine, we also have students from different countries, as training will be in Ukrainian, Russian, English, and even Spanish, and all study materials are in English!
After the course, you will learn:
- Why math and statistics are so important to data science;
- Why is your code not working;
- Fundamentals of linear algebra, calculus, probability theory, and statistics;
- How to read the complex math equations that underlie all machine learning algorithms;
- How to understand the mathematics of machine learning algorithms;
- How to think abstractly.
- Lectures 19
- Quizzes 1
- Duration 8 week
- Skill level beginner
- Language Ukrainian, Russian, English
- Students 0
- Assessments Yes
- Introduction to Probability Theory
- Distributions of Random Variables
- Main Characteristics of Distribution
- Outliers in the Data. Introduction to Information Theory
- Results Approximation; Confidence Intervals
- Null Hypothesis Significance Testing, part 1
- Null Hypothesis Significance Testing, part 2
- Null Hypothesis Significance Testing, part 3