The Data Science Workshop: Learn how you can build machine learning models and create your own real-world data science projects, 2nd Edition
Idioma: inglés
Editorial: Packt Publishing, 2020
- Tapa blanda
- Nuevo
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Vendedor de AbeBooks desde 25 de marzo de 2015
Condición: Nuevo
EUR 48,66
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In.
N° de ref. del artículo ria9781800566927_new
- Título
- The Data Science Workshop: Learn how you can build machine learning models and create your own real-world data science projects, 2nd Edition
- Autor
- Anthony So; Thomas V. Joseph; Robert Thas John; Andrew Worsley; Dr. Samuel Asare
- Editorial
- Packt Publishing
- Año de publicación
- 2020
- Estado
- New
- Encuadernación
- Encuadernación de tapa blanda
- Idioma
- inglés
- ISBN 10
- 1800566921
- ISBN 13
- 9781800566927
- Edición
- 2ª Edición
Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms
Key Features
- Gain a full understanding of the model production and deployment process
- Build your first machine learning model in just five minutes and get a hands-on machine learning experience
- Understand how to deal with common challenges in data science projects
Book Description
Where there's data, there's insight. With so much data being generated, there is immense scope to extract meaningful information that'll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you'll open new career paths and opportunities.
The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You'll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you'll get hands-on with approaches such as grid search and random search.
Next, you'll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You'll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.
By the end of this book, you'll have the skills to start working on data science projects confidently. By the end of this book, you'll have the skills to start working on data science projects confidently.
What you will learn
- Explore the key differences between supervised learning and unsupervised learning
- Manipulate and analyze data using scikit-learn and pandas libraries
- Understand key concepts such as regression, classification, and clustering
- Discover advanced techniques to improve the accuracy of your model
- Understand how to speed up the process of adding new features
- Simplify your machine learning workflow for production
Who this book is for
This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.
Table of Contents
- Introduction to Data Science in Python
- Regression
- Binary Classification
- Multiclass Classification with RandomForest
- Performing Your First Cluster Analysis
- How to Assess Performance
- The Generalization of Machine Learning Models
- Hyperparameter Tuning
- Interpreting a Machine Learning Model
- Analyzing a Dataset
- Data Preparation
- Feature Engineering
- Imbalanced Datasets
- Dimensionality Reduction
- Ensemble Learning
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.
Robert Thas John is a data engineer with a career that spans two decades. He manages a team of data engineers, analysts, and machine learning engineers – roles that he has held in the past. He leads a number of efforts aimed at increasing the adoption of machine learning on embedded devices through various programs from Google Developers and ARM Ltd, which licenses the chips found in Arduinos and other microcontrollers. He started his career as a software engineer with work that has spanned various industries. His first experience with embedded systems was in programming payment terminals.
Andrew David Worsley is an independent consultant and educator with expertise in the areas of machine learning, statistics, cloud computing, and artificial intelligence. He has practiced data science in several countries across a multitude of industries including retail, financial services, marketing, resources, and healthcare.
Dr. Samuel Asare is a professional engineer with enthusiasm for Python programming, research, and writing. He is highly skilled in applying data science methods to the extraction of useful insights from large data sets. He possesses solid skills in project management processes. Samuel has previously held positions, in industry and academia, as a process engineer and a lecturer of materials science and engineering respectively. Presently, he is pursuing his passion for solving industry problems, using data science methods, and writing.
“Acerca de” puede pertenecer a otra edición de este título.
Ria Christie Collections
Uxbridge, Reino Unido
Vendedor de AbeBooks desde 25 de marzo de 2015
Tarifas de envío de Reino Unido a Estados Unidos de America
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