Practical Machine Learning with Scikit-Learn

Learn the most powerful machine learning algorithms in under an hour
Free tutorial
Rating: 4.3 out of 5 (210 ratings)
10,969 students
1hr 7min of on-demand video
English
English [Auto]

How to implement regression, classification and boosting algorithms
Which algorithms work best for a given dataset
Data preprocessing

Requirements

  • Basic python knowledge
  • Google Colab account

Description

Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it's most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.

Algorithms we'll go over (in order):

  • Linear Regression

  • Polynomial Regression

  • Multiple Linear Regression

  • Logistic Regression

  • Support Vector Machines

  • Decision Trees

  • Random Forest

  • Principle Component Analysis

  • Gradient Boosting

  • XGBoost

Who this course is for:

  • People looking to get into AI but don't know where to start
  • People who want to build accurate models as quickly as possible

Instructor

Self Taught Programmer And Learning Enthusiast
Adam Eubanks
  • 4.4 Instructor Rating
  • 10,658 Reviews
  • 214,509 Students
  • 7 Courses

I am a self taught programmer and learning enthusiast. My expertise is mainly in Artificial Intelligence, Ruby on Rails web development, Python and Linux. I hope that my courses will help students learn things that I had difficulty with in an easier and more fun way. These courses are meant to be short, sweet and quick to the point.

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