About the class

You can take this course even if you don't have knowledge about data science, if you have a weak foundation in mathematics and statistics, and even if you are a non-major. It's a course made easy for beginners to understand without losing interest based on my study know-how, starting as a non-major in literature and working as a data scientist.

The theory is as simple as possible, right from the beginning of the lecture Substantial outputBeing able to try making it. This is the core and goal of this course.

Course effect

• Various predictive models can be implemented through basic machine learning algorithms.
• Even if you don't know Python, you can learn Python skills naturally.
• You'll learn how each machine learning algorithm works.

Recommended target

• Applicants for data-related jobs other than math/statistics/computer science majors
• People with no coding experience, little statistical knowledge, but interested in machine learning/data science
• Those who want to use their own data and apply it to machine learning

Notes before taking the course

• This course is aimed at people who do not have a theoretical background in Python or machine learning. Therefore, content that is somewhat easier may be included for those who have already studied the subject to some extent.
• Since the course is conducted on Google Colab, there is no need to install separate software if you only have a Google account.

3 reasons why this class is special

❶ I'll start with the hard work of coding

While coding, there will sometimes be parts that go awry without explanation. Don't spend too much time falling in love with it.

You “Huh? What's this? Why is it written like this?” In most of the parts I do, I learn the principle naturally as I learn one more at a time later. Follow along slowly, at least until you've completed the entire course once.

❷ More efficient than any other course

Until now, it must have been difficult to find a basic Python course like this. Most of the courses are aimed at programmers rather than data analysis or machine learning. Therefore, this course is much more efficient than other basic Python courses.

❸ I captured errors and minor typos as much as possible

Why is teaching Python difficult?

Obviously, I followed the same thing, but there was a strange error. However, since I don't know the cause of the error and I don't know how to handle it, I just get stressed and then turn it off. Even if I look at the error message, I don't really understand it.

In this lesson, I'll show you the errors I've unintentionally faced, and I'll also show you how to fix them.