Scikit-learn is one of the most powerful packages that top data scientists prefer for machine learning. Powerful data analysis and machine learning require fast, accurate computations, and scikit-learn’s packages make building powerful machine learning models super-easy!

This course is targeted at those new to scikit-learn or with some basic knowledge. You will start with generating synthetic data for building a machine learning model, pre-process the data with scikit-learn, and build various supervised and unsupervised models. You will then deep-dive into implementing various optimization techniques like cross-validation, feature selection, regularization, and also dimensionality reduction techniques.

By the end of this course, you will be able to build your own machine learning models and take your data analysis skills to the next level!

About the Author

Sahiba Chopra is an experienced data scientist with over 4 years of experience working on machine learning projects across a diverse set of industries. She has worked on predictive analytics, anomaly detection, credit risk modelling and recommendation engines. As a self-taught data scientist who has undertaken numerous training initiatives herself, she knows and understands what you are looking for and the concepts that will help you most in your data science projects.

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