AI News, Introduction to machine learning in Python with scikit-learn (video series)

Introduction to machine learning in Python with scikit-learn (video series)

In the data science course that I teach for General Assembly, we spend a lot of time using scikit-learn, Python's library for machine learning.

I love teaching scikit-learn, but it has a steep learning curve, and my feeling is that there are not many scikit-learn resources that are targeted towards machine learning beginners.

Thus I decided to create a series of scikit-learn video tutorials, which I launched in April in partnership with Kaggle, the leading online platform for competitive data science!

My goal with this series is to help motivated individuals to gain a thorough grasp of both machine learning fundamentals and the scikit-learn workflow.

Setting up Python for machine learning: scikit-learn and IPython Notebook

Read more about this video here: IPython notebook shown in the video is available on GitHub: RESOURCES ==Six reasons why I recommend scikit-learn: design for machine learning software: you teach Python or R for data science?: installation: installation: installation: documentation: Notebook tutorials: Markdown:'s Python course:'s Python class: for Informatics: SUBSCRIBE!

scikit-learn video #2: Setting up Python for machine learning

Last Wednesday, I introduced my new weekly video series, 'Introduction to machine learning with scikit-learn'.

Over the next few months, you'll learn how to perform effective machine learning using Python's scikit-learn library in order to advance your data science skills.

In next week's video, we'll load a famous dataset into scikit-learn, discuss how machine learning can be used with this data, and cover scikit-learn's four key requirements for input data.