Switch to the store?

Fundamentals of Machine Learning with scikit-learn [Video]

More Information
  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs for Linear Regression
  • Build a data model 
  • Understand how a data model works
  • Understand strategies for hierarchical clustering
  • Ensemble learning with decision trees
  • Learn to tune the parameters of Support Vector machines
  • Implement clusters to a dataset

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge.

In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.

The code bundle for this video course is available at - https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning-with-scikit-learn

Style and Approach

An easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning.

  • Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
  • Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
  • Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.
Course Length 2 hours 33 minutes
ISBN 9781789134377
Date Of Publication 28 Mar 2018


Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.