Anyone considering a career move into data science and/or ML engineering may also find this book to be a great first step toward their goal. ML practitioners will likely find this book to be too basic for their needs, but they may find discussions on some of the tools being used to be helpful if they are unfamiliar.

No prior knowledge of ML or a specific programming language is required, but readers will find the book a little easier to read with some basic knowledge of programming concepts, Python, and SQL. We include references to additional foundational material within context throughout the book. In addition to ML concepts and use case–based examples, you will also explore different tools such as Jupyter Notebooks and basic use of the Linux terminal.

What Is and Isn’t in This Book

This book was created as a first step for those who wish to become ML practitioners, not as a book to turn you into an ML expert. We do not cover the theory of ML in detail, nor do we cover all of the topics from statistics and mathematics needed to be a successful data scientist. We cover the theory that is needed for the projects discussed in this book as a way to ease you into working on ML projects, but going farther than that would be beyond the scope here. We do, however, give many references to resources where you can dive deeper if you are interested in doing so.

Chapters 2 and 3 discuss many different types of data that can be used in ML problems and different tools that can be used in practice. However, no single book can cover every single circumstance with every available tool. We focus on use cases with structured data and only pursue a light discussion around ML for unstructured data in Chapters 2 and 9. Some of the most exciting applications (AI-powered chatbots and image generation, for example) use unstructured data, but in practice most applications of ML in business and industry focus on problems involving structured data.

In terms of tools, we focus on a narrow range of tools so that you can focus on the business use cases. Packages in Python such as NumPy, Seaborn, Pandas, scikit-learn, and TensorFlow are popular across all industries, and we cover those alongside many of the use cases in this book. Jupyter Notebooks are also an industry standard used to interactively run Python code in a notebook environment.

We use Google Colab, a free Jupyter Notebook service, for running our notebooks. Additionally, we will use other Google Cloud tools, such as Vertex AI AutoML for no-code ML model training and BigQuery for SQL data analysis and training ML models using SQL. Other major cloud providers, such as Microsoft Azure and Amazon Web Services (AWS), offer similar services for running Jupyter Notebooks, AutoML, analyzing data with SQL, and training ML models using SQL. We highly encourage and recommend that you explore the other tools that we mention but do not use here. Links for more information and documentation are included throughout the entire book.