Book Image

Python Machine Learning Tips, Tricks, and Techniques [Video]

By : Valeriy Babushkin
Book Image

Python Machine Learning Tips, Tricks, and Techniques [Video]

By: Valeriy Babushkin

Overview of this book

<p>Machine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for machine learning. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.</p> <p>It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on.</p> <p>Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox.</p> <p>By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.</p> <p>All the code and supporting files for this course are available on GitHub at:&nbsp;<a href="https://github.com/PacktPublishing/Python-Machine-Learning-Tips-Tricks-and-Techniques" target="_blank">https://github.com/PacktPublishing/Python-Machine-Learning-Tips-Tricks-and-Techniques</a></p> <h1>Style and Approach</h1> <p>We practice real datasets from different fields, progressively increasing our expertise and putting new tools at our disposal. With a combination of these tools, almost any machine learning problem can be solved much faster and with far better overall results.</p>
Table of Contents (5 chapters)
Chapter 5
Boost Your Overall Model Robustness
Content Locked
Section 2
Regularizing Model to Avoid Overfitting
Regularization helps to prevent over fitting and thus generalize better on new data. - Build a basic catboost classifier - Build catboost classifier with regularization - Compare result for both methods