Before we start

Are you a budding machine learning engineer using Python, wondering what steps to take after mastering the basics? Perhaps you're a researcher exploring traditional machine learning algorithms for your thesis, a data scientist in your first role seeking to elevate model performance, or a leader transitioning into data science who wants to confidently engage with technical teams. This book is crafted precisely for you. It serves as a second course, aiming to answer the pressing question: "What do I do now to improve my machine learning models?"

Our goal is to make you self-sufficient in the fine art of developing highly accurate machine learning models that are production-ready. We'll guide you through the different stages of a machine learning project—from advanced feature engineering to comprehensive model explanation. By learning these fundamental building blocks, you'll become a better-educated machine learning engineer, a more insightful researcher, a capable consultant, and a confident leader in the field of artificial intelligence and machine learning.

This book is neither heavy on mathematical notation nor overloaded with code. Instead, we focus on explaining theoretical nuances, often aided by plots and analysis results, to provide a hands-on feel of projects from inception to completion. Supplemental code is available on the accompanying GitHub page, allowing you to delve deeper into how each analysis was conducted.

We've organized the content to mirror the practical execution of a machine learning project, starting from feature engineering and moving towards model explanation. In Chapter 11, we delve into methods beyond traditional feature engineering and selection, introducing denoising techniques for signal processing data. While we cover multiple approaches to improving model performance, it's important to acknowledge that there's no silver bullet in machine learning. Even after employing techniques like feature engineering, feature extraction, feature selection, and ensembling, you might not achieve the desired model performance. Factors like data inadequacy and quality issues—topics beyond the scope of this book—can lead to project challenges. To illustrate these points, we'll examine four example datasets: two showcasing machine learning successes and two highlighting failure scenarios.

Deep learning models often eliminate the need for feature engineering, especially with unstructured data like images, text, audio, and video. However, for structured tabular data, mastering feature engineering and knowing how to enrich your dataset by creating higher-level features from original ones is crucial. Understanding which features to retain can decisively impact your project's outcome. This book makes a sincere attempt to educate you on these different steps involved in training a machine learning model within the realms of feature engineering and feature selection.

We assume you're already familiar with fundamental concepts like regression and classification, as well as common machine learning techniques such as linear regression, random forests, and logistic regression. You should also be comfortable with different cost functions like Root Mean Square Error (RMSE), F1 score, precision, and recall. This book is tailored for those who have tried their hand at developing models and are seeking guidance to elevate model performance to higher levels.

Although the concepts discussed are applicable across various programming languages, this book is specifically written for Python users. To support this, several algorithms mentioned have been developed from scratch and open-sourced as four separate Python libraries by the author. We expect you to have a basic understanding of libraries like Scikit-learn for machine learning, Pandas and NumPy for data manipulation, and Matplotlib and Seaborn for data visualization.

For experienced consultants, we introduce methods and techniques that are less commonly heard of or used, including those that were not previously available in Python. For leaders in data science, this book will help you gain perspective on how to advocate for your models by utilizing model explainability techniques.

It's important to note that this book focuses on model development after data cleaning has been performed and you have a clean dataset. We do not cover how to clean data, handle outliers, or deal with missing data. Numerous valuable resources already exist on these topics, and they are outside the scope of this book.