<p>You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. What do you do?</p> <p>Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge.</p> <p>Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.</p> <p>All the code and supporting files are available on GitHub at <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Troubleshooting-Python-Machine-Learning" target="blank">https://github.com/PacktPublishing/Troubleshooting-Python-Machine-Learning</a></p> <h2>Style and Approach</h2> <p>The course is full of hands-on instructions, interesting and illustrative visualizations, and, clear explanations from a data scientist. It is packed full of useful tips and relevant advice. Throughout the course, we maintain a focus on practicality and getting things done, not fancy mathematical theory.</p>