Machine learning is a technical science and, like any technical subject, uses a mathematical language to formulate ideas. A growing number of solutions are trying to automate the whole process of machine learning, but if a person does not understand the mathematical formalism underlying the algorithms, it is impossible to test and debug models that can lead to false conclusions.
Python is data scientists’ preferred programming language. If machine learning researchers decide to open source their work they will most likely do it in python. Therefore, the course starts by introducing python concepts and packages that are useful for data analysis. This part of the program also describes data structures, relational and non-relational databases, means of interacting with databases, manipulating data and merging datasets from different sources.
This module starts with an introduction to machine learning: how it is organized, what are the sub branches of machine learning, fundamental differences between these approaches and types of problems they are designed to solve.
Probability and statistics are essential for people who work with Data, especially in Data Analysis and or Data Science. During this course you will be introduced to the basics, such as what is a random variable, probability of its occurrence and probability mass/density function. Next, more complex topics. You will be able to perform exploratory data analysis to draw insights from the raw data. In addition to this, you will understand the math behind A/B testing using statistical hypothesis testing framework, that will help with decision making.
Automated tests are crucial for cost reduction and quick release. This course will teach you how automated tests benefit business, the different types of tests you can create, and how they fit into broader business processes.
Python is one of the most popular programming languages. It is widely used for back end development, software development and data science. The course starts by introducing python concepts and then dives deeper in how to read/write files in Python, access Internet data (API requests) and relational databases (SQL). You will earn the basics of OOP and will be able to define your own classes and functions. The course also covers the introduction to Git to keep track of your code changes and collaborate with your project team.