The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition

Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms

Key Features

• Ideal for those getting started with machine learning for the first time
• A step-by-step machine learning tutorial with exercises and activities that help build key skills
• Structured to let you progress at your own pace, on your own terms
• Use your physical print copy to redeem free access to the online interactive edition

Book Description

You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You'll learn from real examples that lead to real results.

Throughout The Supervised Learning Workshop, you'll take an engaging step-by-step approach to understand supervised learning. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding.

Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book.

Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You'll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

What you will learn

• Get to grips with the fundamental of supervised learning algorithms
• Discover how to use Python libraries for supervised learning
• Learn how to load a dataset in pandas for testing
• Use different types of plots to visually represent the data
• Distinguish between regression and classification problems
• Learn how to perform classification using K-NN and decision trees

Who this book is for

Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Supervised Learning Workshop is ideal for those with a Python background, who are just starting out with machine learning. Pick up a Workshop today, and let Packt help you develop skills that stick with you for life.

Table of Contents

1. Fundamentals of Supervised Learning Algorithms
2. Exploratory Data Analysis and Visualization
3. Linear Regression
4. Autoregression
5. Classification Techniques
6. Ensemble Modeling
7. Model Evaluation

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on market/business analytics including machine learning and forecasting. His hands-on abilities include R coding, Keras, AWS & Azure machine learning, Office suite, writing, market research, due diligence, and employee development and training.

Ashish Ranjan Jha has more than 4 years of experience in data science and machine learning ranging from building data ingestion and cleaning pipelines, exploratory data analysis, classic machine learning modeling and deep learning, to deploying models into production systems. He uses the Python ecosystem, Pandas, Spark, and Dask for data crunching, Sklearn for classical machine learning, and Keras, Tensorflow, and Pytorch for deep learning.

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven medtech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his PhD in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years' experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.