Discover the power of SQL with "Machine Learning for Everyone." Dive into a captivating case study of an online movie rental company, exploring a database filled with customer information, movie ratings, actor backgrounds, and more. Unlock the ability to make data-driven decisions as you learn to harness the potential of SQL queries. Investigate customer preferences, analyze customer engagement, and uncover sales development opportunities. Gain an edge with SQL extensions for online analytical processing (OLAP), simplifying the extraction of crucial insights from multidimensional aggregated data. Take your data analysis skills to new heights and equip yourself with the tools to thrive in the world of machine learning.

This course will teach you the fundamentals of machine learning classification. You will learn how to create models that predict a class (positive/negative sentiment, loan default risk) from input features (text of reviews, financial data). You will become familiar with logistic regression, decision trees, boosting, and stochastic gradient ascent. You will also learn how to handle missing data and evaluate models using precision-recall metrics. All of this will be done in Python. Enroll now and start your journey into the world of machine learning classification!

This course provides a comprehensive introduction to the foundations of machine learning. Through a series of case studies, you will gain hands-on experience with a range of machine learning techniques, from regression and classification to deep learning and recommender systems. You will learn how to identify potential applications, select the appropriate machine learning task, represent data as features, assess model quality, and build end-to-end applications. By the end of the course, you will be able to apply machine learning methods to a wide range of domains.

Data scientists and data analysts can benefit from this course on Practical Machine Learning. It covers the basics of building and applying prediction functions, with an emphasis on practical applications. Topics include training and test sets, overfitting, error rates, and a range of model-based and algorithmic machine learning methods. Learn how to collect data, create features, apply algorithms, and evaluate results. Get the skills you need to make accurate predictions.