| Sumario: | 6+ Hours of Video Instruction Learn the main concepts and techniques used in modern machine learning through numerous examples written in scikit-learn Overview Machine Learning with scikit-learn LiveLessons is your guide to the scikit-learn library, which provides a wide range of algorithms in machine learning that are unified under a common and intuitive Python API. Most of the dozens of classes provided for various kinds of models share the large majority of the same calling interface. Quite often you can easily substitute one algorithm for another with very little or no change in your underlying code. This enables you to explore the problem space quickly and often to arrive at an optimal–or at least satisficing–approach to your problem domain or datasets. The scikit-learn library is built on the foundations of the numeric Python stack. It uses NumPy for its fundamental data structures and optimized performance, and it plays well with pandas and matplotlib. It is free software under a BSD license. The great bulk of machine learning programming in Python is done with scikit-learn—at least outside the specialized domain of deep neural networks. About the Instructor David Mertz has been involved with the Python community for 20 years, with data science, (under various previous names) and with machine learning since way back when it was more likely to be called “artificial intelligence.” He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups. He has also written quite a bit about Python: the column Charming Python for IBM developerWorks, for many years; Text Processing in Python (Addison-Wesley, 2003); and two short books for O’Reilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them. Skill Level Intermediate Learn How To Use various machine learning techniques Explore a dataset Perform various types of classification Use regression, clustering, and hyperparameters Use feature engineering and feature selection Implement data pipelines Develop robust train/test splits Who Should Take This Course Programmers and statisticians interested in using Python and the scikit-learn library to implement machine learning Course Requirements Programming experience Table of Contents Introduction Lesson 1: What Is Machine Learning? Lesson 2: Exploring a Dataset Lesson 3: Classification Lesson 4: Regression Less...
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