Introduction to Machine Learning with Scikit-Learn

Learn the three main techniques of machine learning: regression, classification and clustering, using Scikit-Learn

This course introduces machine learning covering the three main techniques used in industry: regression, classification, and clustering.

What you’ll learn

  • In this course you will learn: Machine Learning and Scikit-Learn.
  • You will be able to recognize problems that can be solved with Machine Learning.
  • Select the right technique (is it a classification problem? a regression? needs preprocessing?).
  • Train and evaluate regression models with Scikit-Learn to forecast numerical quantities..
  • Train and evaluate classification models with Scikit-Learn to predict categories..
  • Use clustering techniques to group your data and discover insights..

Course Content

  • Introduction –> 7 lectures • 21min.
  • Regression Problems in Machine Learning: Theory –> 5 lectures • 11min.
  • Introduction to Scikit-Learn –> 4 lectures • 8min.
  • Regression Hands-on Lab –> 9 lectures • 26min.
  • Classification Problems in Machine Learning –> 15 lectures • 32min.
  • Classification Hands-on Lab –> 6 lectures • 18min.
  • Clustering Problems in Machine Learning –> 9 lectures • 26min.
  • Clustering Hands-on Lab –> 7 lectures • 10min.

Introduction to Machine Learning with Scikit-Learn


  • Previous experience programming in Python is advised.
  • Our free Pandas Masterclass can be useful.

This course introduces machine learning covering the three main techniques used in industry: regression, classification, and clustering.

It is designed to be self-contained, easy to approach, and fast to assimilate.

You will learn:

  • What machine learning is
  • Where machine learning is used in industry
  • How to recognize the technique you should use
  • How to solve regression problems to predict numerical quantities
  • How to solve classification problems to predict categorical quantities
  • How to use clustering to group your data and discover new insights

The course is designed to maximize the learning experience for everyone and includes 50% theory and 50% hands-on practice. It includes labs with hands-on exercises and solutions.

No software installation required. You can run the code on Google CoLab and get started right away.

This course is the fastest way to get up to speed in machine learning and Scikit Learn.


Why Machine Learning?

Machine Learning has taken the world by a storm in the last 10 years, revolutionizing every company and empowering many applications we use every day.

Here are some examples of where you can find machine learning today: recommender systems, image recognition, sentiment analysis, price prediction, machine translation, and many more!

There are over 3000 job announcements requiring Scikit Learn in the United States alone, and almost 80000 jobs mentioning machine learning in the US. Machine Learning engineers can easily earn six figure salaries in major cities, and companies are investing Billions of dollars in developing their teams.

Even if you already have a job, understanding how machine learning works will empower you to start new projects and get visibility in your company.


Why Scikit Learn?

  • It’s the best Python library to learn machine learning
  • Simple, yet powerful API for predictive data analysis
  • Used in many industries: tech, biology, finance, insurance
  • Built on standard libraries such as NumPy, SciPy, and Matplotlib