The The Data Science Workshop

Learn how you can build machine learning models and create your own real-world data science projects, 2nd Edition

; Thomas V. Joseph ; Robert Thas John ; Andrew Worsley ; Dr. Samuel Asare

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 DescriptionWhere there's data, there's insight. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 466,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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 DescriptionWhere 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 forThis 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.

Fakta

Innholdsfortegnelse

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

Om forfatteren

Anthony So is an outstanding leader with more than 13 years of experience. He is recognized for his analytical skills and data-driven approach for solving complex business problems and driving performance improvements. 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 Google developer expert in machine learning. His day job involves working as a data engineer on the Google Cloud Platform by building, training, and deploying large-scale machine learning models. He also makes decisions about how to store and process large amounts of data. He has more than 10 y