Exploratory data analysis for machine learning

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the Professional Certificate content. In this course, you will understand the importance of good, quality data. You will learn common techniques for data acquisition, data cleaning, feature engineering, and data preparation for preliminary analysis and hypothesis testing. By the end of this course, you should be able to: Acquire data from a variety of data sources: SQL, NoSQL databases, APIs, Cloud Describe and use common feature selection and feature engineering techniques Work with categorical, ordinal, and missing values ​​features Use different techniques for detecting and removing outliers Explain why feature scaling is important and use different scaling techniques Who should take this course? This course is designed for aspiring data scientists interested in gaining hands-on experience with machine learning and artificial intelligence in a business setting. What skills should you have? To get the most out of this course, you should be familiar with programming in the Python development environment, as well as have a fundamental understanding of calculus, linear algebra, probability, and statistics.