Practical Machine Learning with Scikit-Learn

Learn the most powerful machine learning algorithms in under an hour

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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



Your Instructor(s)


Adam Eubanks
Adam Eubanks

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.


Course Curriculum


  Introduction
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  Regression
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  Classification
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  Boosting and Optimization
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Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.