Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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What Is Microsoft Azure Machine Learning Studio?

Microsoft Azure Machine Learning Studio (henceforth referred to as MAML) is an online collaborative, drag‐and‐drop tool for building machine learning models. Instead of implementing machine learning algorithms in languages like Python or R, MAML encapsulates the most‐commonly used machine learning algorithms as modules, and it lets you build learning models visually using your dataset. This shields the beginning data science practitioners from the details of the algorithms, while at the same time offering the ability to fine‐tune the hyperparameters of the algorithm for advanced users. Once the learning model is tested and evaluated, you can publish your learning models as web services so that your custom apps or BI tools, such as Excel, can consume it. What's more, MAML supports embedding your Python or R scripts within your learning models, giving advanced users the opportunity to write custom machine...