Module presentation

In this module, we are going to see the most important supervised models in machine learning, both in theory and in practice using Python programming language.

What will I learn with this module?

Completing this module you will learn the most common supervised models of machine learning and how to apply them in Python using sklearn library.

The models we are going to see are:

  • Linear models
  • Decision trees
  • k-nearest neighbors
  • Naive Bayes
  • Support Vector Machines
  • Feedforward neural networks

Who is this course for?

This course has been made for:

  • aspiring, junior and senior data scientists
  • machine learning engineers
  • data analysts
  • researchers
  • students
  • everybody who is interested in machine learning and data science


In order to benefit from the course, it's useful to have this previous knowledge:

In order to achieve the necessary knowledge of data pre-processing techniques used supervised machine learning, we strongly suggest to attend Data pre-processing for Machine Learning in Python course first.

What will I get once I enroll in the course?

After you buy the course, you'll have complete access to the video lessons of this course and you'll be able to start discussions with the teacher and the other students using the comment section under every lesson.

Your Instructor

My name is Gianluca Malato, I'm Italian and have a Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome.

I'm a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.

I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).

I've written many articles about Machine Learning, R and Python and I've been a Top Writer on in Artificial Intelligence category.

Course contents

  Linear models
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  Decision trees
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  K-nearest neighbors
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  Naive Bayes
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  Support Vector Machines
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  Neural Networks
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  End of module
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Frequently Asked Questions

Does the course have a start and a finish date?

No. Once you enroll, you can follow the recorded video lessons when you want.

How can I pay for the course?

You can pay with a credit card using Teachable's payment gateway.

How can I follow the lessons?

Once you pay for your enrollment, you can access the recorded video lessons of the course when you want from your computer using this website. These videos are given in streaming, so you'll need to connect to this website and have an Internet connection in order to watch them. After you create your account and log in, you can use the My Courses link in top of every page to see all the courses you have enrolled in.

I don't know anything about machine learning. Can I access the course?

Sure. As soon as you can ensure the prerequisites, you can follow this course.

Why this course is different from the other ML courses on the web?

Because in this course we're going to see, practically, how to apply Supervised Machine Learning. There is still a theory part in every lesson because you need to know what you are doing, but the course focuses on the practical approach to ML. This kind of approach can help a data scientist apply, in a real-life job, the concepts taught in this course and this is a value we really believe in.

What language will be used?

During this course, the spoken and written language is English.

What if I'm not satisfied?

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.

Join the course