Hands-On Machine Learning with Python and Scikit-Learn

Hands-On Machine Learning with Python and Scikit-Learn

Updated Feb 07, 2019

Machine learning and artificial intelligence are the new big data - at least as far as buzzwords in the workplace go. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python along with the Scikit-Learn library. We begin our journey by observing the end result of a Machine Learning deployment before moving back to the fundamentals and into exploratory data analysis. Moving on, we learn to develop complex pipelines and techniques for building custom transformer objects for feature extraction, manipulation, and other effective data cleansing techniques. Finally, we discover how to select a model, apply optimal hyper-parameters, and deploy it. This video course highlights clean coding techniques, object-oriented transformer design and best practices in Machine Learning while using the Scikit-Learn library and also maintaining a focus on practicality and re-usability, ensuring these techniques can be applied to Machine Learning projects of any size. Style and Approach: An easy-to-follow implementation of the scikit-learn library that will help you get started with the effective Machine Learning techniques using Python. 

Target Audience 

This course is aimed at students and data-scientists with prior Python programming experience and keen to upgrade their Machine Learning skills using Python. A basic familiarity with, or exposure to, some level of statistics is recommended, but not required. 

Business Outcomes  

  • Deep dive into Machine Learning using the most advanced tools and the Scikit library.
  • Develop complex pipelines and process data through manipulation, extraction, and data-cleansing techniques.
  • Clean coding techniques and best practices in Machine Learning which are applicable to any scalable Machine Learning projects.