Apply effective learning algorithms to real-world problems using scikit-learn

Apply effective learning algorithms to real-world problems using scikit-learn

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.

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.

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.

Experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library.

Experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library.

This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field.

Key Features Understand and apply machine learning methods using an extensive set of R packages such as XGBOOSTUnderstand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised LearningImplement advanced concepts in machine learning with this example-rich guide Book Description This book will teach you advanced ...(展开全部)

Cory Leismester 具有十多年量化管理经验，目前在银行业担任高级量化管理经理，负责构建市场营销和监管模型。曾在礼来公司就职16年，负责销售、市场调查、精益六西格玛、营销分析、新产品预测等工作。