Implement scikit-learn into every step of the data science pipeline

About This BookUse Python and scikit-learn to create intelligent applicationsDiscover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domainA practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learnWho This Book Is For

If you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the course for you. No previous experience with machine-learning algorithms is required.

What You Will LearnReview fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metricsClassify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naive BayesUse Decision Trees to explain the main causes of certain phenomena such as passenger survival on the TitanicEvaluate the performance of machine learning systems in common tasksMaster algorithms of various levels of complexity and learn how to analyze data at the same timeLearn just enough math to think about the connections between various algorithmsCustomize machine learning algorithms to fit your problem, and learn how to modify them when the situation calls for itIncorporate other packages from the Python ecosystem to munge and visualize your datasetImprove the way you build your models using parallelization techniquesIn Detail

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.

Style and Approach

Implement scikit-learn using engaging examples and fun exercises, and with a gentle and friendly but comprehensive “learn-by-doing” approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of scikit-learn.

Trent Hauck