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Über dieses Buch

Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.
Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs.
By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.What You Will Learn
Gain insights into machine learning concepts
Work on real-world applications of machine learningLearn concepts of model selection and optimizationGet a hands-on overview of Python from a machine learning point of view

Who This Book Is For
Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

Inhaltsverzeichnis

Frontmatter

2017 | OriginalPaper | Buchkapitel

Chapter 1. Statistics and Probability

Danish Haroon

2017 | OriginalPaper | Buchkapitel

Chapter 2. Regression

Danish Haroon

2017 | OriginalPaper | Buchkapitel

Chapter 3. Time Series

Danish Haroon

2017 | OriginalPaper | Buchkapitel

Chapter 4. Clustering

Danish Haroon

2017 | OriginalPaper | Buchkapitel

Chapter 5. Classification

Danish Haroon

Backmatter

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