Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World (Rheinwerk Computing)
Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following compelling use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its business impact over time. With sample code for download, this book gives you everything needed to implement machine learning models that solve real business problems!
Practical introduction to applied machine learning across three real-world use casesSelect and implement the right machine learning model for your business problemEvaluate model results and monitor your models long term
Data Preparation
The first step is to understand your data. Learn about different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.
Model Selection
Choose the machine learning model that fits your problem! Follow a structured model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, and clustering.
Evaluation and Iteration
Assess and improve the quality of your model! Apply a variety of validation metrics, enhance interpretability to avoid black box code, and iterate through feature engineering and adding or removing data.
Implementation and Monitoring
Your model is ready—now put it to work! Learn how to implement your model to generate predictions, monitor its performance over time, and measure its impact for your business.
From the Publisher
The practical guide to applying machine learning in the real world!
Develop ML models to solve business problems!
Machine learning models analyze patterns in data, optimize their parameters during the training process, and improve the quality of their predictions over time. However, the theory behind machine learning is only the beginning. Learning how to apply these models in a business setting is the key to creating value for your organization—and this book is your how-to guide! After setting up the software and accounts you need to get started, you’ll learn about three business problems corresponding to real-world use cases. These use cases will serve as your blueprint throughout the book as you walk through the end-to-end applied machine learning workflow, from data preparation and model selection to evaluation and monitoring.
Understand your data using techniques such as visualization and correlation analysis, and prepare it for modeling by identifying inaccuracies, performing dimensionality reduction, and more. Pick your machine learning model by exploring each algorithm, from regression to decision trees to clustering. Evaluate your results with validation metrics, iterate on your model, and monitor its performance!
With each use case, you’ll get unique datasets that demonstrate each part of applied machine learning in a practical business context. All code is available for direct download and via a GitHub repository. Get all the tools you need to master applied machine learning for your business!
Real-world use cases Data exploration Data cleaning Model decision framework Machine learning algorithms Validation metrics Model iteration Monitoring
Inside the book
If you’re new to applying machine learning to real-world business scenarios, this book is for you. Whether you’re a data analyst, data scientist, software engineer, or machine learning enthusiast, you’ll find value within these pages. However, a foundational understanding of machine learning and experience with Python are prerequisites for reading this book.
This book includes:
Hands-on guidance for end-to-end applied machine learningReal-world use cases and datasetsDiagrams, charts, screenshots, definitions, and background informationCode samples available for downloadA comprehensive index
Meet the author
Jason Hodson is a senior data analytics consultant in the people analytics space. He has worked at multiple large companies as an internal consultant, helping stakeholders solve business problems using data. He combines strong business and consulting skills with technical skills in SQL, Python, and machine learning. One of his passions is helping others learn and grow in the data space. Jason studied marketing at Winona State University and has a master’s degree in business analytics from the University of St. Thomas (MN).
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What is it? (What does it teach)
A beginner’s guide to developing AI applications! A beginner’s guide to Python! Develop your own gen AI applications with Python! The practical guide to AI pair programming! The ultimate Python 3 guide for all levels!
What you’ll learn?
This guide teaches you how to build your own AI applications from scratch, starting with no-code tools and progressing to basic Python programming, covering key AI methods and tools like AutoKeras, ChatGPT, and DALL-E. Learn the fundamentals of Python programming—from variables and loops to functions and modules—and apply your skills to build applications, databases, and graphical user interfaces. Learn to to work with pretrained LLM and NLP models on Hugging Face and LangChain, create vector databases, implement retrieval-augmented generation, and add agentic systems using frameworks like crewAI and AutoGen. Learn to optimize your software development with AI, using tools like ChatGPT, GitHub Copilot, and OpenHands to generate code, refactor programs, debug scripts, and enhance tasks like database design, unit testing, and documentation. Learn to write effective Python code. Cover core concepts like functions, modularization, and object orientation, and explore data types. Dive into advanced topics, including Django and GUIs.
Who is this book for?
Developers (professionals and students) Professional programmers without prior experience in Python. Python developers Developers Python programmers
Key Features?
Step-by-step instructions, screenshots and diagrams, and downloadable code for hands-on practice. Bite-sized exercises, a continuous game project, and downloadable code examples. Step-by-step instructions and detailed listings, with downloadable code examples. Step-by-step instructions and examples. Step-by-step instructions and downloadable code are available.
Level of difficulty?
Readers will complete a simple no-code exercise with a sample dataset, then apply their knowledge to a more complex, beginner-friendly exercise. Designed for beginners with no prior Python experience, this book teaches how to use the language to develop simple applications. Python programming is a prerequisite for this book because the language is central to most AI systems. This book helps experienced programmers enhance efficiency by integrating AI pair programming tools into their workflow. Beginners to professional programmers.