By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.

It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data.

This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

May 8, 2018 · Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.

Jan 13, 2025 · Machine learning and AI can help you make sense of large data sets, identify patterns, and make predictions. This can help to gain a competitive edge by making better decisions faster than your competitors.

Apply machine learning to improve targeted marketing & strategic decision-making. Analyze & forecast customer behaviors using advanced algorithms.

Apr 4, 2024 · Data science studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions.

The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn.

Feb 3, 2025 · Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models capable of predicting outcomes and classifying information without human intervention.