data science · Machine Learning · Python

Video Course: Practical Python Data Science Techniques

I’m happy to announce the recent release of my second video course,
Practical Python Data Science Techniques published with Packt Publishing.

VideoCourse-Cover

Links:

This video course follows my first introductory course (Data Analysis with Python) and provides the audience with recipe-like solutions to common Data Science problems.

In particular, with about 2.5 hours of material, the video course covers the following topics:

  1. Exploring Your Data
    This section covers some of the most common techniques related to loading data, performing exploratory analysis and cleaning your data to get them in the right shape.
  2. Dealing with Text
    describes the common pre-processing techniques that you need to deal with text, from tokenisation to normalisation, to calculating word frequencies.
  3. Machine Learning Problems
    describes the most common Machine Learning problems and how to tackle them using scikit-learn.
  4. Time Series and Recommender Systems
    The last section groups some miscellanous topics, in particulr Time Series Analysis and the basics to implement a recommender system.

More details about the content of the course are available on the PacktPub’s page, and of course you can check out the code examples on my GitHub (links on top of this page).

If you are a beginner you may also be interested in my other video course, Data Analysis with Python (see video course on PacktPub.com, course material on GitHub and course overview on this blog).

@MarcoBonzanini

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