Machine Learning and Generative AI for Marketing E-Book

Yoon Hyup Hwang

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 550

Veröffentlichungsjahr: 2024

Beschreibung

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience.
This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales.
Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards
By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.

Leseprobe

Machine Learning and Generative AI for Marketing

Take your data-driven marketing strategies to the next level using Python

Yoon Hyup Hwang

Nicholas C. Burtch

Machine Learning and Generative AI for Marketing

Copyright © 2024 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Senior Publishing Product Manager: Tushar Gupta

Acquisition Editor – Peer Reviews: Swaroop Singh

Project Editor: Amisha Vathare

Content Development Editor: Shruti Menon

Copy Editor: Safis Editing

Technical Editor: Karan Sonawane

Proofreader: Safis Editing

Indexer: Manju Arasan

Presentation Designer: Ganesh Bhadwalkar

Developer Relations Marketing Executive: Meghal Patel

First published: August 2024

Production reference: 1230824

Published by Packt Publishing Ltd.

Grosvenor House

11 St Paul’s Square

Birmingham

B3 1RB, UK.

ISBN: 978-1-83588-940-4

www.packt.com

Contributors

About the authors

Yoon Hyup Hwang is a data science and engineering leader who has authored multiple books on applied ML and data science. He has over a decade of experience and expertise in delivering extremely high ROI data products and solutions that result in multi-million-dollar annual recurring revenue and savings across various industries that include finance, insurance, ads and marketing, manufacturing, and supply chain. He holds an MSE degree in Computer and Information Technology from the University of Pennsylvania and a BA in Economics from the University of Chicago.

I would like to start by thanking my beautiful wife, Sunyoung. By sacrificing our weekends and family times so that I could work on writing this book over the past several months, she played a critical role in getting this book done and published.

To my family, I would like to thank you for your consistent love, support, and belief in me. I would not be where I am now without you.

I would also like to thank everyone on my publishing team at Packt who helped me uphold the highest quality standard.

Lastly, I would like to thank everyone reading this book. I hope you enjoy it and find it helpful!

Nicholas C. Burtch, PhD, is a recognized data science researcher and thought leader with over ten years of experience in leading complex, data-driven projects. He has an extensive track record of deploying end-to-end ML solutions for understanding large-scale structured and unstructured data in industries ranging from finance to scientific research. Nick has published dozens of peer-reviewed research articles that have received thousands of citations and is a US patent holder. He received his PhD and MS in Chemical Engineering from the Georgia Institute of Technology and holds a BS in Chemical Engineering from the University of Michigan.

I would like to express my deepest gratitude to the people closest to me who supported me throughout the creation of this book. Your encouragement and understanding have been invaluable. A special thanks to my loved ones for their patience and understanding throughout this journey.

I am also thankful to the Packt Publishing team for their guidance and commitment to excellence.

Finally, to the readers, I hope this book provides you with valuable insights that you can leverage in your own professional journey.

About the reviewers

Dr. Na’im R. Tyson is a seasoned data scientist with extensive experience in natural language processing (NLP) and machine learning (ML). He holds a PhD in Linguistics from Ohio State University and an MS in Computational Linguistics from Georgetown University. His expertise spans developing ML models, implementing data ingestion pipelines, and refining NLP applications. Currently an Adjunct Assistant Professor at New York University, Dr. Tyson is passionate about education and holds several patents in language learning systems and keyword extraction methods, focusing on practical applications that drive meaningful results.

Oluwole Fagbohun currently serves as the Vice President of Engineering and Environmental Data at ChangeBlock, where he leads initiatives to combat climate change through innovative ML solutions. He holds a master’s degree in data science, has published several peer-reviewed papers, and is a published author. He is a frequent speaker at multiple conferences and universities and has mentored participants at various hackathons. As the founder of Readrly, an EdTech company, Oluwole leverages AI techniques to enhance children’s reading skills by providing captivating stories from around the globe.

Oluwole is passionate about playing chess and cherishes spending quality time with his family. Originally hailing from Nigeria, he now resides in London with his wife and three children.

I would like to extend my heartfelt gratitude to my wife and children for their unwavering support, now and always. Special thanks to my parents for their enduring love and encouragement.

Beta reader

Konstantin Pikal is a PhD Candidate at LUISS University in Rome, specializing in marketing technologies. With over a decade of experience as a marketing leader, he has developed an expertise in data-driven marketing, data analytics, and content marketing. His research focuses on bridging the gap between marketing research and practical applications, aiming to bring academic insights into real-world marketing strategies and to classrooms around the world.

Join our book’s Discord space

Join our Discord community to meet like-minded people and learn alongside more than 5000 members at:

https://packt.link/genai

Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

The Evolution of Marketing in the AI Era and Preparing Your Toolkit

The evolution of marketing with AI/ML

The pre-AI era of marketing

The advent of digital marketing

The integration of AI/ML in marketing

Core data science techniques in marketing

Predictive analytics

Understanding customer data

A/B testing and experimentation

Segmentation and targeting

Customer lifetime value modeling

Integrating AI for enhanced insights

Setting up the Python environment for AI/ML projects

Installing the Anaconda distribution

Installing essential Python libraries for AI/ML

Integrating your environment with JupyterLab

Verifying your setup

Navigating the Jupyter notebook

Training your first ML model

Step 1: Importing the necessary libraries

Step 2: Loading the data

Step 3: Exploratory data analysis

Step 4: Preparing the data for ML

Step 5: Training a model

Step 6: Evaluating the model

Summary

Decoding Marketing Performance with KPIs

Understanding marketing KPIs

Awareness stage

Engagement stage

Conversion stage

Retention stage

Loyalty stage

Computing and visualizing KPIs from data with Python

Conversion rate

Overall conversion rate

Demographics and conversion rate

Sales channel and conversion rate

CLV

Overall CLV

Geolocation and CLV

Product and CLV

CPA

Overall CPA

Sales channel and CPA

Promotions and CPA

ROI

Overall ROI

ROI per sales channel

ROI per promotion

Correlations across KPIs

Tracking marketing performance with KPIs

Choosing the right visualization for KPIs

Ongoing KPI tracking

Summary

Unveiling the Dynamics of Marketing Success

Why people engage in regression analysis

Target variable

Continuous variables

Categorical variables

Factorize

Categorical

Dummy variables

Regression analysis

Interaction variables

Why people convert with decision tree interpretation

Target variable

Continuous versus categorical variable

Decision tree analysis

Why people churn with causal inference

Causal effect estimation

Causal model

Estimation

Refutation

Graphical causal model

Causal influence

Anomaly attribution

Summary

Harnessing Seasonality and Trends for Strategic Planning

Time series analysis basics

Basic time series trends

Moving averages

Autocorrelation

Product trends

Trend and seasonality decomposition

Additive time series decomposition

Multiplicative time series decomposition

Time series forecasting models

ARIMA

Training the ARIMA model

ARIMA model diagnostics

Forecasting with the ARIMA model

Prophet time-series modeling

Training a Prophet model

Forecasting with a Prophet model

Other time-series models

Summary

Enhancing Customer Insight with Sentiment Analysis

Introduction to sentiment analysis in marketing

The significance of sentiment analysis

Advancements in AI and sentiment analysis

Practical example: Twitter Airline Sentiment dataset

Preparing data for sentiment analysis

Traditional NLP techniques for data preparation

Cleaning text data

Tokenization and stop word removal

Lemmatization

Class imbalance

Evaluating class balance

Addressing class imbalance

GenAI for data augmentation

Performing sentiment analysis

Building your own ML model

Feature engineering

Model training

Model evaluation

Classification report

Using pre-trained LLMs

Implementing pre-trained models

Evaluating model performance

Translating sentiment into actionable insights

Creating your own dataset

Collecting twitter data

Collecting data from other platforms

Performing NER on a dataset for a fictional retailer

Understanding topics and themes

Using word clouds

Discovering latent topics with LDA

Temporal trends: tracking the brand narrative

Mapping sentiments using geospatial analysis

Summary

Leveraging Predictive Analytics and A/B Testing for Customer Engagement

Predicting customer conversion with tree-based algorithms

Tree-based machine learning algorithms

Building random forest models

Target and feature variables

Training a random forest model

Predicting and evaluating random forest model

Gradient boosted decision tree (GBDT) modeling

Training GBDT model

Predicting and Evaluating GBDT Model

Predicting customer conversion with deep learning algorithms

Train and test sets for deep learning models

Wide neural network modeling

Training the wide neural network model

Predicting and evaluating wide neural network model

Deep neural network modeling

Training deep neural network model

Predicting and evaluating the deep neural network model

Conducting A/B testing for optimal model choice

Simulating A/B Testing

Two-tailed T-test

Summary

Personalized Product Recommendations

Product analytics with market basket analysis

Apriori algorithm – finding frequent itemsets

Association rules

Collaborative filtering

User-based collaborative filtering

Recommending by the most similar customer

Recommending by the top products bought by similar customers

Item-based collaborative filtering

Recommending by the most similar items

Recommending by the purchase history

Other frequently used recommendation methods

Summary

Segmenting Customers with Machine Learning

One-time versus repeat customers

The need to retain customers

Analyzing the impact of retaining customers

Customer segmentation with purchase behaviors

K-means clustering

Without log transformation

With log transformation

Silhouette score

Customer segmentation with product interests

Summary

Creating Compelling Content with Zero-Shot Learning

Fundamentals of generative AI

A probabilistic approach

Foundational models

Generative adversarial networks

Variational autoencoders

Long short-term memory networks

Transformers

When GenAI is the right fit

Introduction to pre-trained models and ZSL

Contextual embeddings

Semantic proximity

Pre-trained models

Model weights

Model architecture

Preprocessing steps

Zero-shot learning

Mechanics of learning and prediction

Output parameters

ZSL for marketing copy

Preparing for ZSL in Python

Creating an effective prompt

Example 1: Product descriptions

Example 2: Blog post titles

Example 3: Social media captions

Impact of parameter tuning

Summary

Enhancing Brand Presence with Few-Shot Learning and Transfer Learning

Navigating FSL

Understanding FSL through meta-learning

Implementing model-agnostic meta-learning in marketing

Overcoming challenges in FSL

Navigating transfer learning

The mechanics of transfer learning

Transfer learning using Keras

Transfer learning using API services

Overcoming challenges in transfer learning

Applying FSL to improve brand consistency

Benchmarking with ZSL and FSL

Developing an email marketing campaign

Step 1: Initial email creation

Step 2: Collect and analyze initial metrics and responses

Step 3: Iterative refinement

Step 4: Continued feedback integration

Summary

Micro-Targeting with Retrieval-Augmented Generation

Introduction to RAG for precision marketing

How RAG works

Mathematical model of RAG retrieval

The importance of data in RAG

Data freshness

Data specificity

Understanding the retrieval index

Indexing strategies

Data curation and updating

RAG implementation challenges

Applications of RAG in marketing

Building a knowledge retrieval system for marketing with LangChain

Introduction to LangChain

Understanding the external dataset

Designing the retrieval model with LangChain

Install and connect to Elasticsearch

Indexing data in Elasticsearch

Data ingestion into Elasticsearch

Integrating with LangChain using GPT

Implementing RAG for micro-targeting based on customer data

Determining the campaign strategy

Message timing

Choosing the brand

Using LangChain for micro-targeting

Case study 1: Targeted product discounts

Case study 2: Product upselling

Case study 3: Real-time content customization for bpw.style

Summary

The Future Landscape of AI and ML in Marketing

Consolidating key AI and ML concepts

Next-generation AI technologies in marketing

From RAG to ReAct

How ReAct works

Applications in marketing

Model architecture advances

Diffusion models

Neural radiance fields

Capsule networks

Multi-modal GenAI

Technical foundations

Multi-modal applications

AR and VR as emerging digital platforms

The role of ML in AR for marketing

Personalized AR experiences

Geolocation-based AR campaigns

Seamless product integration

The role of ML in VR for marketing

Immersive storytelling and brand experiences

Behavioral data insights

Virtual storefronts and events

Summary

Ethics and Governance in AI-Enabled Marketing

Ethical considerations in AI/ML for marketing

Model transparency and explainability

Model explainability tools

Bias mitigation

Addressing training data bias

Algorithmic fairness techniques

Diversity in model evaluation data

Grounding for LLMs

Chain-of-thought reasoning

Balancing privacy with personalization

Federated learning

Differential privacy and anonymization

Governance and regulatory compliance

Intellectual property protection

Data and model licensing and attribution

Internal data ownership and security

Data management and collection practices

Ethical governance frameworks

Internal AI ethics committees

AI policy development and reporting

Continuous training and awareness

Regulatory compliance

General Data Protection Regulation (GDPR)

California Consumer Privacy Act (CCPA)

Global regulations and standards

Industry-specific guidelines

Summary

Other Books You May Enjoy

Index

Landmarks

Cover

Index

Preface

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it’s a necessity. AI/ML integration in marketing strategies represents a paradigm shift towards more data-driven, efficient, and impactful marketing practices. With the emergence of data science, businesses can now gain an in-depth understanding of the mechanics of their marketing successes and failures, understanding customer behaviors and interactions with unprecedented clarity. Moreover, the rise of generative AI (GenAI) is creating new opportunities for the facile creation of highly personalized, engaging content that resonates with the target audience on a whole new level. This book provides a comprehensive toolkit for harnessing the power of ML and GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales.

Starting with the basics of Python for data analysis and progressing to more sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. We will use hands-on examples to show you how you can harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. We’ll also explore the ethical implications of AI, which will help you ensure that your marketing strategies are not only effective but also responsible and compliant with current standards.

By the conclusion of this book, you will have gained a robust understanding of various data science, ML, and GenAI techniques tailored specifically for marketing. Armed with this knowledge, you will be equipped to design, launch, and manage marketing campaigns that are not just successful but also cutting-edge, ensuring your business remains at the forefront of the digital marketing revolution.

Who this book is for

This book targets a diverse group of professionals at the intersection of technology and marketing, including:

Marketing professionals at any level seeking to leverage AI/ML for data-driven decision-making and enhance their customer engagement strategiesData scientists and analysts in the marketing domain looking to apply advanced AI/ML techniques to solve real-world marketing challengesML engineers and software developers aiming to build or integrate AI-driven tools and applications for marketing purposesBusiness leaders and entrepreneurs who must understand the impact of AI on marketing to drive innovation and maintain their competitive advantage in today’s landscape

Each reader is presumed to have a foundational proficiency in Python programming and a basic to intermediate grasp of ML principles and data science methodologies. They are likely already in or aspire to be in roles where the application of AI/ML directly influences marketing outcomes and business strategies.

What this book covers

Chapter 1, The Evolution of Marketing in the AI Era and Preparing Your Toolkit, explores the evolution of marketing in the AI era, core AI/ML techniques shaping marketing’s future, and setting up a Python environment for marketing projects.

Chapter 2, Decoding Marketing Performance with KPIs, explains that every data-driven or AI/ML-driven marketing strategy starts with optimization and enhancement goals tied to key performance metrics (KPIs). Understanding and analyzing these KPIs is critical for evaluating the effectiveness of marketing campaigns and strategies. This chapter discusses how to identify and compute crucial marketing KPIs, leveraging them for explanatory data analysis, and using these insights to drive marketing decisions.

Chapter 3, Unveiling the Dynamics of Marketing Success, investigates the underlying dynamics contributing to marketing success. By utilizing data science and AI/ML techniques, you can gain deep insights into the factors driving successful marketing campaigns. The topics covered include analyzing customer interactions, identifying successful marketing patterns, and optimizing strategies based on data-driven insights.

Chapter 4, Harnessing Seasonality and Trends for Strategic Planning, discusses seasonality and trends, which play a vital role in shaping marketing strategies. This chapter focuses on leveraging AI/ML to identify and capitalize on predictable fluctuations in consumer behavior and market dynamics. By understanding these patterns, you can strategically plan and execute campaigns that align with consumer interests and market conditions.

Chapter 5, Enhancing Customer Insight with Sentiment Analysis, discusses customer sentiment, which is a valuable indicator of brand perception and customer satisfaction. This chapter explores how sentiment analysis, powered by AI/ML, can enhance customer insights. You will learn how to analyze customer feedback, reviews, and social media interactions to tailor your strategies to improve customer experience and engagement.

Chapter 6, Leveraging Predictive Analytics and A/B Testing for Customer Engagement, covers predictive analytics and A/B testing, which are essential tools for enhancing customer engagement. This chapter explains how to use AI/ML models to predict customer behavior and engagement levels. It also covers the design and execution of effective A/B tests to optimize marketing decisions and improve user experiences.

Chapter 7, Personalized Product Recommendations, explores personalized product recommendations, which significantly enhance the likelihood of purchase by aligning offerings with customer preferences and behaviors. This chapter covers various techniques for generating personalized recommendations, including market basket analysis, collaborative filtering, and other recommendation methods.

Chapter 8, Segmenting Customers with Machine Learning, discusses customer segmentation, which is a critical aspect of targeted marketing. This chapter discusses using ML to segment customers based on various factors, such as purchase behavior, demographics, and interests. It covers techniques like K-means clustering and leveraging large language models (LLMs) for deeper customer segmentation insights.

Chapter 9, Creating Compelling Content with Zero-Shot Learning, introduces zero-shot learning (ZSL), a technique that allows AI models to generate relevant marketing content without prior direct examples. ZSL enhances creativity and speed, allowing you to produce content for new categories and contexts using learned patterns and knowledge extrapolation. You will find practical examples and best practices for integrating ZSL into marketing strategies, showcasing how to create dynamic content for product descriptions, blog posts, and social media.

Chapter 10, Enhancing Brand Presence with Few-Shot Learning and Transfer Learning, covers few-shot learning (FSL), which is a method that’s used to adapt AI models to new tasks using only a small number of labeled examples.

This chapter covers the principles of FSL, including meta-learning, which enable models to generalize effectively to new scenarios. The practical examples demonstrate how you can apply FSL to marketing campaigns, emphasizing its importance in capturing nuanced aspects of brand ethos and refining marketing strategies based on customer feedback.

Chapter 11, Micro-Targeting with Retrieval-Augmented Generation, this chapter explores retrieval-augmented generation (RAG) as a tool for precision marketing, combining GenAI with real-time information retrieval. This hybrid approach allows the creation of highly personalized content by accessing and incorporating current data during the generation process. This chapter details RAG’s operational framework and its application in micro-targeting, showcasing how it can improve consumer engagement and conversion by providing contextually appropriate and accurate content.

Chapter 12, The Future Landscape of AI and ML in Marketing, consolidates key AI and ML concepts from previous chapters and discusses their future applications in marketing. It covers emerging technologies such as multi-modal GenAI, advanced model architectures, and the integration of AI with augmented and virtual reality. These advancements promise to create more dynamic, responsive, and personalized marketing strategies. This chapter provides insights into how these technologies will shape the future of marketing, enabling more immersive consumer experiences and innovative marketing practices.

Chapter 13, Ethics and Governance in AI-Enabled Marketing, addresses the ethical considerations and governance challenges associated with AI technologies in marketing. It explores data privacy, algorithmic bias, and the need for transparency, emphasizing their impact on consumer trust and brand integrity. The chapter also covers major regulatory frameworks such as GDPR and CCPA, providing strategies for responsible AI deployment, model transparency, and compliance. Finally, it discusses practical guidelines for mitigating bias, ensuring data privacy, and establishing robust governance structures to promote ethical AI use in marketing.

To get the most out of this book

To maximize the benefits of this book, it is recommended that you have basic familiarity with Python. However, the book is structured to accommodate a wide range of expertise levels, from beginners to advanced practitioners. Here are a few tips to help you get the most out of this book:

Engage with the code examples: Practical examples are provided throughout the book to illustrate key concepts. Actively engaging with these examples by running the code and experimenting with variations will deepen your understanding and enhance your skills.Apply the knowledge: Try to apply the techniques and strategies discussed in the book to your own marketing projects. This practical application will help reinforce the concepts and demonstrate their real-world relevance.Stay updated: The fields of AI and ML are rapidly evolving. Staying up to date with the latest advancements, tools, and best practices through continuous learning and professional development will ensure that you remain at the forefront of this exciting domain.

The line graphs in this book may look slightly different from those generated by the provided code. This is because the graphs have been optimized to ensure clarity and ease of understanding; feel free to play around with visualization to achieve the type of outcome you need!

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-and-Generative-AI-for-Marketing. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781835889404.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “Select System info from the Administration panel.”

Important notes or suggestions appear like this.

Tips and tricks appear like this.

Get in touch

Feedback from our readers is always welcome.

General feedback: Email [email protected] and mention the book’s title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you reported this to us. Please visit http://www.packtpub.com/submit-errata, click Submit Errata, and fill in the form.

Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.

If you are interested in becoming an author: If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, please visit http://authors.packtpub.com.

Share your thoughts

Once you’ve read Machine Learning and Generative AI for Marketing, we’d love to hear your thoughts! Please click here to go straight to the Amazon review page for this book and share your feedback.

Your review is important to us and the tech community and will help us make sure we’re delivering excellent quality content.

Download a free PDF copy of this book

Thanks for purchasing this book!

Do you like to read on the go but are unable to carry your print books everywhere?

Is your eBook purchase not compatible with the device of your choice?

Don’t worry, now with every Packt book you get a DRM-free PDF version of that book at no cost.

Read anywhere, any place, on any device. Search, copy, and paste code from your favorite technical books directly into your application.

The perks don’t stop there, you can get exclusive access to discounts, newsletters, and great free content in your inbox daily.

Follow these simple steps to get the benefits:

Scan the QR code or visit the link below:

https://packt.link/free-ebook/9781835889404

Submit your proof of purchase.That’s it! We’ll send your free PDF and other benefits to your email directly.