<p>Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.</p> <p>This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models.</p> <p>Finally, you'll explore dimensionality reduction with various parameters.</p> <h1>Style and Approach</h1> <p>This course consists of practical videos on scikit-learn that target novices as well as intermediate users. It explores technical issues in depth, covers additional protocols, and supplies many more real-life examples so that you are able to implement scikit-learn in your daily life.</p>