Python Machine Learning Case Studies e-bog
509,93 DKK
(inkl. moms 637,41 DKK)
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 Ca...
E-bog
509,93 DKK
Forlag
Apress
Udgivet
27 oktober 2017
Genrer
Computer programming / software engineering
Sprog
English
Format
pdf
Beskyttelse
LCP
ISBN
9781484228234
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 LearnGain 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 viewWho This Book Is ForData scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.