Search icon Close icon
Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Checkout

Change country

Modal Close icon
Country selected Country selected
country flag United States
Country selected
country flag Great Britain
Country selected
country flag India
Country selected
country flag Germany
Country selected
country flag France
Country selected
country flag Canada
Country selected
country flag Russia
Country selected
country flag Spain
Country selected
country flag Brazil
Country selected
country flag Australia
Country selected
country flag Singapore
Country selected
country flag Hungary
Country selected
country flag Ukraine
Country selected
country flag Luxembourg
Country selected
country flag Estonia
Country selected
country flag Lithuania
Country selected
country flag South Korea
Country selected
country flag Turkey
Country selected
country flag Switzerland
Country selected
country flag Colombia
Country selected
country flag Taiwan
Country selected
country flag Chile
Country selected
country flag Norway
Country selected
country flag Ecuador
Country selected
country flag Indonesia
Country selected
country flag New Zealand
Country selected
country flag Cyprus
Country selected
country flag Denmark
Country selected
country flag Finland
Country selected
country flag Poland
Country selected
country flag Malta
Country selected
country flag Czechia
Country selected
country flag Austria
Country selected
country flag Sweden
Country selected
country flag Italy
Country selected
country flag Egypt
Country selected
country flag Belgium
Country selected
country flag Portugal
Country selected
country flag Slovenia
Country selected
country flag Ireland
Country selected
country flag Romania
Country selected
country flag Greece
Country selected
country flag Argentina
Country selected
country flag Netherlands
Country selected
country flag Bulgaria
Country selected
country flag Latvia
Country selected
country flag South Africa
Country selected
country flag Malaysia
Country selected
country flag Japan
Country selected
country flag Slovakia
Country selected
country flag Philippines
Country selected
country flag Mexico
Country selected
country flag Thailand
Country selected
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
Home > Data > Machine Learning > scikit-learn Cookbook , Second Edition
scikit-learn Cookbook , Second Edition
scikit-learn Cookbook , Second Edition

scikit-learn Cookbook , Second Edition: Over 80 recipes for machine learning in Python with scikit-learn , Second Edition

Arrow left icon
Profile Icon Trent Hauck
Arrow right icon
€32.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
Paperback Nov 2017 374 pages 2nd Edition
eBook
€8.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Trent Hauck
Arrow right icon
€32.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
Paperback Nov 2017 374 pages 2nd Edition
eBook
€8.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

scikit-learn Cookbook , Second Edition

Pre-Model Workflow and Pre-Processing

In this chapter we will see the following recipes:

  • Creating sample data for toy analysis
  • Scaling data to the standard normal distribution
  • Creating binary features through thresholding
  • Working with categorical variables
  • Imputing missing values through various strategies
  • A linear model in the presence of outliers
  • Putting it all together with pipelines
  • Using Gaussian processes for regression
  • Using SGD for regression

Introduction

What is data, and what are we doing with it?

A simple answer is that we attempt to place our data as points on paper, graph them, think, and look for simple explanations that approximate the data well. The simple geometric line of F=ma (force being proportional to acceleration) explained a lot of noisy data for hundreds of years. I tend to think of data science as data compression at times.

Sometimes, when a machine is given only win-lose outcomes (of winning games of checkers, for example) and trained, I think of artificial intelligence. It is never taught explicit directions on how to play to win in such a case.

This chapter deals with the pre-processing of data in scikit-learn. Some questions you can ask about your dataset are as follows:

  • Are there missing values in your dataset?
  • Are there outliers (points far away from the others) in your set?
  • What are the variables...

Creating sample data for toy analysis

If possible, use some of your own data for this book, but in the event you cannot, we'll learn how we can use scikit-learn to create toy data. scikit-learn's pseudo, theoretically constructed data is very interesting in its own right.

Getting ready

Very similar to getting built-in datasets, fetching new datasets, and creating sample datasets, the functions that are used follow the naming convention make_*. Just to be clear, this data is purely artificial:

from sklearn import datasets
datasets.make_*?

datasets.make_biclusters
datasets.make_blobs
datasets.make_checkerboard
datasets.make_circles
datasets.make_classification
...

To save typing, import the datasets module as d, and numpy...

Scaling data to the standard normal distribution

A pre-processing step that is recommended is to scale columns to the standard normal. The standard normal is probably the most important distribution in statistics. If you've ever been introduced to statistics, you must have almost certainly seen z-scores. In truth, that's all this recipe is about—transforming our features from their endowed distribution into z-scores.

Getting ready

The act of scaling data is extremely useful. There are a lot of machine learning algorithms, which perform differently (and incorrectly) in the event the features exist at different scales. For example, SVMs perform poorly if the data isn't scaled because they use a distance...

Creating binary features through thresholding

In the last recipe, we looked at transforming our data into the standard normal distribution. Now, we'll talk about another transformation, one that is quite different. Instead of working with the distribution to standardize it, we'll purposely throw away data; if we have good reason, this can be a very smart move. Often, in what is ostensibly continuous data, there are discontinuities that can be determined via binary features.

Additionally, note that in the previous chapter, we turned a classification problem into a regression problem. With thresholding, we can turn a regression problem into a classification problem. This happens in some data science contexts.

Getting ready

...

Working with categorical variables

Categorical variables are a problem. On one hand they provide valuable information; on the other hand, it's probably text—either the actual text or integers corresponding to the text—such as an index in a lookup table.

So, we clearly need to represent our text as integers for the model's sake, but we can't just use the id field or naively represent them. This is because we need to avoid a similar problem to the Creating binary features through thresholding recipe. If we treat data that is continuous, it must be interpreted as continuous.

Getting ready

The Boston dataset won't be useful for this section. While it's useful for feature binarization, it...

Imputing missing values through various strategies

Data imputation is critical in practice, and thankfully there are many ways to deal with it. In this recipe, we'll look at a few of the strategies. However, be aware that there might be other approaches that fit your situation better.

This means scikit-learn comes with the ability to perform fairly common imputations; it will simply apply some transformations to the existing data and fill the NAs. However, if the dataset is missing data, and there's a known reason for this missing data—for example, response times for a server that times out after 100 ms—it might be better to take a statistical approach through other packages, such as the Bayesian treatment via PyMC, hazards models via Lifelines, or something home-grown.

...

A linear model in the presence of outliers

In this recipe, instead of traditional linear regression we will try using the Theil-Sen estimator to deal with some outliers.

Getting ready

First, create the data corresponding to a line with a slope of 2:

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

num_points = 100
x_vals = np.arange(num_points)
y_truth = 2 * x_vals
plt.plot(x_vals, y_truth)

Add noise to that data and label it as y_noisy:

y_noisy = y_truth.copy()
#Change y-values of some points in the line
y_noisy[20:40] = y_noisy[20:40] * (-4 * x_vals[20:40]) - 100

plt.title("Noise in y-direction")
plt.xlim([0,100])
plt.scatter(x_vals, y_noisy,marker='x')
...

Putting it all together with pipelines

Now that we've used pipelines and data transformation techniques, we'll walk through a more complicated example that combines several of the previous recipes into a pipeline.

Getting ready

In this section, we'll show off some more of pipeline's power. When we used it earlier to impute missing values, it was only a quick taste; here, we'll chain together multiple pre-processing steps to show how pipelines can remove extra work.

Let's briefly load the iris dataset and seed it with some missing values:

from sklearn.datasets import load_iris
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
iris_data = iris.data
mask = np.random.binomial...

Using Gaussian processes for regression

In this recipe, we'll use a Gaussian process for regression. In the linear models section, we will see how representing prior information on the coefficients was possible using Bayesian ridge regression.

With a Gaussian process, it's about the variance and not the mean. However, with a Gaussian process, we assume the mean is 0, so it's the covariance function we'll need to specify.

The basic setup is similar to how a prior can be put on the coefficients in a typical regression problem. With a Gaussian process, a prior can be put on the functional form of the data, and it's the covariance between the data points that is used to model the data, and therefore, must fit the data.

A big advantage of Gaussian processes is that they can predict probabilistically: you can obtain confidence bounds on your predictions. Additionally...

Using SGD for regression

In this recipe, we'll get our first taste of stochastic gradient descent. We'll use it for regression here.

Getting ready

SGD is often an unsung hero in machine learning. Underneath many algorithms, there is SGD doing the work. It's popular due to its simplicity and speed—these are both very good things to have when dealing with a lot of data. The other nice thing about SGD is that while it's at the core of many machine learning algorithms computationally, it does so because it easily describes the process. At the end of the day, we apply some transformation on the data, and then we fit our data to the model with a loss function.

...
Left arrow icon

Page 1 of 11

Right arrow icon
Download code icon Download Code

Key benefits

  • Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn
  • Perform supervised and unsupervised learning with ease, and evaluate the performance of your model
  • Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm

Description

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.

Who is this book for?

Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book will help you too.

What you will learn

  • Build predictive models in minutes by using scikit-learn
  • Understand the differences and relationships between Classification and Regression, two types of Supervised Learning.
  • Use distance metrics to predict in Clustering, a type of Unsupervised Learning
  • Find points with similar characteristics with Nearest Neighbors.
  • Use automation and cross-validation to find a best model and focus on it for a data product
  • Choose among the best algorithm of many or use them together in an ensemble.
  • Create your own estimator with the simple syntax of sklearn
  • Explore the feed-forward neural networks available in scikit-learn
Estimated delivery fee Deliver to Sweden

Premium delivery 7 - 10 business days

€17.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 16, 2017
Length: 374 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787286382
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
Scikit-learn

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to Sweden

Premium delivery 7 - 10 business days

€17.95
(Includes tracking information)

Product Details

Publication date : Nov 16, 2017
Length: 374 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787286382
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
Scikit-learn

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
START FREE TRIAL
BUY NOW
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW

Frequently bought together

Python Machine Learning, Second Edition
Python Machine Learning, Second Edition
Read more
Sep 2017 622 pages
Full star icon 4.3 (89)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €32.99
€8.99 €26.99
€32.99
scikit-learn Cookbook , Second Edition
scikit-learn Cookbook , Second Edition
Read more
Nov 2017 374 pages
Full star icon 3.7 (3)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €32.99
€8.99 €26.99
€32.99
scikit-learn : Machine Learning Simplified
scikit-learn : Machine Learning Simplified
Read more
Nov 2017 530 pages
Course
Course
€74.99
Stars icon
Total € 140.97
Python Machine Learning, Second Edition
€32.99
scikit-learn Cookbook , Second Edition
€32.99
scikit-learn : Machine Learning Simplified
€74.99
Total € 140.97 Stars icon
Banner background image

Table of Contents

12 Chapters
High-Performance Machine Learning – NumPy Chevron down icon Chevron up icon
High-Performance Machine Learning – NumPy
Introduction
NumPy basics
Loading the iris dataset
Viewing the iris dataset
Viewing the iris dataset with Pandas
Plotting with NumPy and matplotlib
A minimal machine learning recipe – SVM classification
Introducing cross-validation
Putting it all together
Machine learning overview – classification versus regression
Pre-Model Workflow and Pre-Processing Chevron down icon Chevron up icon
Pre-Model Workflow and Pre-Processing
Introduction
Creating sample data for toy analysis
Scaling data to the standard normal distribution
Creating binary features through thresholding
Working with categorical variables
Imputing missing values through various strategies
A linear model in the presence of outliers
Putting it all together with pipelines
Using Gaussian processes for regression
Using SGD for regression
Dimensionality Reduction Chevron down icon Chevron up icon
Dimensionality Reduction
Introduction
Reducing dimensionality with PCA
Using factor analysis for decomposition
Using kernel PCA for nonlinear dimensionality reduction
Using truncated SVD to reduce dimensionality
Using decomposition to classify with DictionaryLearning
Doing dimensionality reduction with manifolds – t-SNE
Testing methods to reduce dimensionality with pipelines
Linear Models with scikit-learn Chevron down icon Chevron up icon
Linear Models with scikit-learn
Introduction
Fitting a line through data
Fitting a line through data with machine learning
Evaluating the linear regression model
Using ridge regression to overcome linear regression's shortfalls
Optimizing the ridge regression parameter
Using sparsity to regularize models
Taking a more fundamental approach to regularization with LARS
References
Linear Models – Logistic Regression Chevron down icon Chevron up icon
Linear Models – Logistic Regression
Introduction
Loading data from the UCI repository
Viewing the Pima Indians diabetes dataset with pandas
Looking at the UCI Pima Indians dataset web page
Machine learning with logistic regression
Examining logistic regression errors with a confusion matrix
Varying the classification threshold in logistic regression
Receiver operating characteristic – ROC analysis
Plotting an ROC curve without context
Putting it all together – UCI breast cancer dataset
Building Models with Distance Metrics Chevron down icon Chevron up icon
Building Models with Distance Metrics
Introduction
Using k-means to cluster data
Optimizing the number of centroids
Assessing cluster correctness
Using MiniBatch k-means to handle more data
Quantizing an image with k-means clustering
Finding the closest object in the feature space
Probabilistic clustering with Gaussian mixture models
Using k-means for outlier detection
Using KNN for regression
Cross-Validation and Post-Model Workflow Chevron down icon Chevron up icon
Cross-Validation and Post-Model Workflow
Introduction
Selecting a model with cross-validation
K-fold cross validation
Balanced cross-validation
Cross-validation with ShuffleSplit
Time series cross-validation
Grid search with scikit-learn
Randomized search with scikit-learn
Classification metrics
Regression metrics
Clustering metrics
Using dummy estimators to compare results
Feature selection
Feature selection on L1 norms
Persisting models with joblib or pickle
Support Vector Machines Chevron down icon Chevron up icon
Support Vector Machines
Introduction
Classifying data with a linear SVM
Optimizing an SVM
Multiclass classification with SVM
Support vector regression
Tree Algorithms and Ensembles Chevron down icon Chevron up icon
Tree Algorithms and Ensembles
Introduction
Doing basic classifications with decision trees
Visualizing a decision tree with pydot
Tuning a decision tree
Using decision trees for regression
Reducing overfitting with cross-validation
Implementing random forest regression
Bagging regression with nearest neighbors
Tuning gradient boosting trees
Tuning an AdaBoost regressor
Writing a stacking aggregator with scikit-learn
Text and Multiclass Classification with scikit-learn Chevron down icon Chevron up icon
Text and Multiclass Classification with scikit-learn
Using LDA for classification
Working with QDA – a nonlinear LDA
Using SGD for classification
Classifying documents with Naive Bayes
Label propagation with semi-supervised learning
Neural Networks Chevron down icon Chevron up icon
Neural Networks
Introduction
Perceptron classifier
Neural network – multilayer perceptron
Stacking with a neural network
Create a Simple Estimator Chevron down icon Chevron up icon
Create a Simple Estimator
Introduction
Create a simple estimator

Recommendations for you

Left arrow icon
LLM Engineer's Handbook
LLM Engineer's Handbook
Read more
Oct 2024 522 pages
Full star icon 4.8 (17)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €54.99
€8.99 €43.99
€54.99
Getting Started with Tableau 2018.x
Getting Started with Tableau 2018.x
Read more
Sep 2018 396 pages
Full star icon 4 (3)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €41.99
€8.99 €32.99
€41.99
Semantic Kernel SDK for Intelligent Applications
Semantic Kernel SDK for Intelligent Applications
Read more
Dec 2024 2hrs 9mins
Video
Video
  • Video Video €8.99
€8.99 €82.99
AI Ecosystem for the Absolute Beginners - Hands-On
AI Ecosystem for the Absolute Beginners - Hands-On
Read more
Dec 2024 4hrs 56mins
Video
Video
  • Video Video €8.99
€8.99 €81.99
AI Product Manager's Handbook
AI Product Manager's Handbook
Read more
Nov 2024 484 pages
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
RAG-Driven Generative AI
RAG-Driven Generative AI
Read more
Sep 2024 334 pages
Full star icon 4.5 (13)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €40.99
€8.99 €32.99
€40.99
Python for Algorithmic Trading Cookbook
Python for Algorithmic Trading Cookbook
Read more
Aug 2024 404 pages
Full star icon 4.6 (15)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €44.99
€8.99 €35.99
€44.99
Artificial Intelligence Engines
Artificial Intelligence Engines
Read more
Nov 2024 217 pages
eBook
eBook
  • eBook eBook €4.99
€4.99 €7.99
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
Read more
Feb 2022 774 pages
Full star icon 4.4 (95)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €41.99
€8.99 €32.99
€41.99
Building LLM Powered Applications
Building LLM Powered Applications
Read more
May 2024 342 pages
Full star icon 4.2 (22)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
Right arrow icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Jose Luis Ramirez Nov 28, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been working with python in my AI projects and I came up upon this great book of recipes that I can use quickly and practically in every stage of my developments. It is ease to use right away and it has reference to the enough amount of theory so you don't have to go to search around for extra info. The book introduces neural networks in a simple way and it has robust OOP for more complex AI projects.
Amazon Verified review Amazon
Rex Jones May 03, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent reference for basic modeling in Python using scikit. Julian Avila has created a great reference that I use it in my class. It's easy to incorporate reading assignments from this book.
Amazon Verified review Amazon
Miss S Betts Apr 22, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Purchase book.Open in cloud reader and start flicking through - and the reader starts displaying the word in a single column down the centre of the screen. Can't find settings to stop it.
Amazon Verified review Amazon

People who bought this also bought

Left arrow icon
Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
Read more
May 2023 456 pages
Full star icon 4.5 (49)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €29.99
€8.99 €23.99
€29.99
Generative AI with LangChain
Generative AI with LangChain
Read more
Dec 2023 368 pages
Full star icon 4 (34)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
Read more
May 2023 286 pages
Full star icon 4.2 (34)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
Deep Learning with TensorFlow and Keras – 3rd edition
Deep Learning with TensorFlow and Keras – 3rd edition
Read more
Oct 2022 698 pages
Full star icon 4.6 (45)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
Machine Learning Engineering with Python
Machine Learning Engineering with Python
Read more
Aug 2023 462 pages
Full star icon 4.6 (36)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
Right arrow icon

About the author

Profile icon Trent Hauck
Trent Hauck
Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
Read more
See other products by Trent Hauck
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela