Data is the driving force of today’s digital economy, and the ability to wrangle, analyze, and model it effectively has become a vital skill across industries. Python’s powerful ecosystem, led by libraries like NumPy, Pandas, and Scikit-learn, enables professionals to transform raw datasets into meaningful insights and build ML solutions that solve real-world problems.
This book offers a practical, hands-on journey into mastering these libraries step-by-step. You will begin with NumPy and Pandas, learning how to manipulate arrays, DataFrames, time series, and large datasets efficiently. The focus then shifts to Scikit-learn, where you will explore classification, regression, clustering, dimensionality reduction, and time series modeling. Along the way, you will explore the practical case studies, including thyroid disease prediction, customer segmentation, and housing price estimation. You will also explore topics such as hyperparameter tuning, ensemble methods, pipelines, and deep neural networks, followed by guidance on deploying ML models with Flask, FastAPI, Docker, and Swagger.
By the end of this book, you will be confident in applying the core data science libraries of Python to real-world problems. You will be able to clean and transform complex datasets, build and optimize robust ML models, and deploy them into production environments as scalable APIs. This book equips you with the practical skills needed to excel in data-driven roles and deliver impactful ML solutions.
WHAT YOU WILL LEARN
● Manipulate arrays and datasets using NumPy and Pandas effectively.
● Preprocess data and build models with Scikit-learn workflows.
● Apply regression, classification, dimensionality reduction, time series forecasting, deep learning, and clustering to real datasets.
● Handle missing values, time series, and large-scale data.
● Optimize performance with hyperparameter tuning and ensemble methods.
● Deploy ML models as scalable RESTful APIs.
WHO THIS BOOK IS FOR
This book is for Python developers, data analysts, system administrators, cloud engineers, aspiring data scientists, and anyone looking to master data wrangling and ML. It is ideal for professionals seeking to transition into data-driven roles and apply practical ML solutions in their jobs.
Gary Hutson is a machine learning specialist and author whose work bridges research, engineering and applied innovation. He began his career as an intelligence analyst, applying statistics and modeling to solve complex crime and justice problems, and went on to become head of machine learning, leading teams and developing AI systems across healthcare, public services, housing insights, private consulting and defense. More recently, he has worked as a senior generative AI engineer, building retrieval augmented generation (RAG) systems for defense and now applies his expertise as a data scientist, using automation to optimize the pharmaceutical supply industry. His technical interests span natural language processing, computer vision, predictive modeling and generative AI, with a focus on making AI system scalable, and impactful. He has co-authored a book and has created a wide range of R and Python packages – including OddsPlotty, ConfusionTableR, MLDataR, modelviz and Combined Alpha-weighted Random Forest Layered Inference Ensemble (CHARLIE) a model built in PyTorch, that are used by ML engineers and data scientists worldwide. With professional certification as a Google certified machine learning engineer and TensorFlow developer, along with a degree in applied business statistics and a master’s in business and finance. Additionally, he is an active blogger on Hutsons-hacks and likes to compete on Kaggle. In his spare time, he is a father and husband, coach for ATFA Brinsley Falcons football team, fan of Nottingham Forest football club and active member in data science communities.
