Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning by Benjamin Johnston
English | 2019 | ISBN: 1789954920 | 404 Pages | PDF | 11 MB

Explore the exciting world of machine learning with the fastest growing technology in the world
Machine learning―the ability of a machine to give right answers based on input data―has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You’ll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.
With the help of fun examples, you’ll gain experience working on the Python machine learning toolkit―from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you’ll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You’ll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.
This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.
By the end of this book, you’ll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
What you will learn

  • Understand the concept of supervised learning and its applications
  • Implement common supervised learning algorithms using machine learning Python libraries
  • Validate models using the k-fold technique
  • Build your models with decision trees to get results effortlessly
  • Use ensemble modeling techniques to improve the performance of your model
  • Apply a variety of metrics to compare machine learning models