Oliver Theobald works as a Senior Operations Specialist for Alibaba Cloud where he works with cloud architects, machine learning engineers and product managers to produce content for customers regarding AI and cloud-based solutions in big data, smart cities, and security. He is also a part-time Instructor for

Oliver is a graduate of the Royal Melbourne Institute of Technology and is an Australian national of British and Austrian descent. Oliver has been living in Asia since 2011. He currently works in both Hangzhou and Beijing, China.

Oliver writes regular technical content on the topics of cloud computing, ICP registration, artificial intelligence, e-commerce, and machine learning. His programming language of choice is Python and in his free time he enjoys studying Chinese, selecting his Fantasy Premier League team each week and listening to historical fiction and non-fiction audiobooks. 


After two AI winters and ongoing battles for funding, we have entered a golden age in industry employment. Complex databases, fast and affordable processing units, and advanced algorithms have rejuvenated established fields of human expertise in mathematics, statistics, computer programming, graphics and data visualization as well as good old problem-solving skills.

In a global job market steadily automated and simplified by Web 2.0 technology, the field of machine learning also provides a professional nirvana for human problem-solving and meaningful work.


Who This Book is For?

As the second title in the Machine Learning for Beginners series, this teaches beginners to code basic machine learning models using Python. The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. If this doesn’t describe your experience or if you need a refresher, key concepts from machine learning in the opening chapter and there are overviews of specific algorithms dispersed throughout this book. For a gentle and more detailed explanation of machine learning theory minus the code, I suggest reading the first book in this series Machine Learning for Absolute Beginners (Second Edition), which is written for a more general audience.

As a practical introduction to coding machine learning models, this book falls short of a complete introduction to programming with Python. Instead, general nuances in the code are explained to enlighten beginners without stalling the progress of experienced programmers. For those new to Python, a basic overview of Python can be found in the Appendix section of this book.


In this step-by-step guide you will learn:

– To code practical machine learning prediction models using a range of supervised learning algorithms including logistic regression, gradient boosting, and decision trees
– Clean and inspect your data using free machine learning libraries
– Visual relationships in your dataset including Heatmaps and Pairplots in just a few lines of simple code
– Develop your expertise at managing data using Python


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