Introduction to Data Science and scikit-learn in Python
Learn how to leverage the power of Python and AI to create and test hypotheses. This course covers basic Python for data science, exploratory data analysis, machine learning libraries, linear regression, and classification models. Apply these skills to estimate diabetes progression and predict heart disease presence/absence.
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We’ll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We’ll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we’ll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you’ll apply a classification model to predict the presence/absence of heart disease from a patient’s health data.
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