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Machine learning and artificial intelligence are the new big data—at least as far as buzzwords in the workplace go. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python along with the Scikit-Learn library.
We begin our journey by observing the end result of a Machine Learning deployment before moving back to the fundamentals and into exploratory data analysis. Moving on, we learn to develop complex pipelines and techniques for building custom transformer objects for feature extraction, manipulation, and other effective data cleansing techniques. Finally, we discover how to select a model, apply optimal hyper-parameters, and deploy it.
This video course highlights clean coding techniques, object-oriented transformer design and best practices in Machine Learning while using the Scikit-Learn library and also maintaining a focus on practicality and re-usability, ensuring these techniques can be applied to Machine Learning projects of any size.
This course uses Python 3.6, and scikit-learn 0.20 while not the latest version available, it provides relevant and informative content for legacy users of Machine Learning and Python.
About the Author
Taylor Smith is a Machine Learning and software development enthusiast with over five years' data science experience. He loves to help businesses find value in Machine Learning by applying interesting computational solutions to challenging business problems. Currently working as a Principal Data Scientist, Taylor is also an active open-source contributor and staunch Pythonista.
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