“Machine learning”, the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers.
Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you will use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
As you work through the numerous examples, you will explore key topics like classification, numeric prediction, and clustering. Along the way, you will be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.
Inside the Book:
- An easy to follow introduction to machine learning
- Automatically classifying data for more precise analysis
- Forecasting values
- Building recommendation engines
Some programming background is helpful, but no prior knowledge of Python or machine learning techniques is required.
Peter Harrington holds Bachelors and Masters Degrees in Electrical Engineering. He worked for Intel Corporation for seven years in California and China. Peter hold four US patents and his work has been published in two academic journals. He is actively involved with research on community detection and runs a software consulting business that focuses on scientific computing solutions.