Python Machine Learning Case Studies

Five Case Studies for the Data Scientist
1. Auflage 2017
ISBN: 978-1-4842-2822-7
Verlag: Apress

Buch, Englisch, 204 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 347 g

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 bookis 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 learning
• Learn concepts of model selection and optimization
• Get 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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Haroon, Danish

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Chapter 1:  Statistics and Probability.- Chapter 2:  Regression.- Chapter 3: Time series models.- Chapter 4: Classification and Clustering.- Chapter 5: Ensemble methods.

Über Autor(innen)

Danish Haroon currently leads the Data Sciences team at Market IQ Inc, a patented predictive analytics platform focused on providing actionable, real-time intelligence, culled from sentiment inflection points. He received his MBA from Karachi School for Business and Leadership, having served corporate clients and their data analytics requirements. Most recently, he led the data commercialization team at PredictifyME, a startup focused on providing predictive analytics for demand planning and real estate markets in the US market. His current research focuses on the amalgam of data sciences for improved customer experiences (CX).