Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
About this ebook
- Understand various machine learning concepts with real-world examples
- Implement a supervised machine learning pipeline from data ingestion to validation
- Gain insights into how you can use machine learning in everyday life
- Understand the concept of supervised learning and its applications
- Implement common supervised learning algorithms using machine learning Python libraries
- Validate models using the k-fold technique
- Build your models with decision trees to get results effortlessly
- Use ensemble modeling techniques to improve the performance of your model
- Apply a variety of metrics to compare machine learning models
Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
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About the author
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 Ph.D. in ML, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds a first-class honors bachelor's degree 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.
