Harvard University - HarvardX: Data Science: Machine Learning

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HarvardX: Data Science: Machine Learning
at
edX
Overview

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques

Duration	8 weeks
Start from	Start Now
Total fee	Free
Mode of learning	Online
Official Website	Explore Free Course
Credential	Certificate

HarvardX: Data Science: Machine Learning
at
edX
Highlights

Earn a certificate from Harvard University
Learn from industry experts

HarvardX: Data Science: Machine Learning
at
edX
Course details

Skills you will learn

Data Science

Who should do this course?

Professionals already working in data science roles who want to further enhance their expertise in machine learning, explore advanced algorithms and techniques, and stay updated with the latest advancements in the field

What are the course deliverables?

Students should gain a solid understanding of fundamental concepts in data science and machine learning, including algorithms, models, optimization techniques and evaluation metrics
Ability to preprocess and clean data, including handling missing values, outliers and normalization, to prepare it for analysis
Understanding of various machine learning algorithms and techniques, along with the ability to select appropriate models based on data characteristics and evaluate their performance using suitable metrics
Ability to deploy machine learning models into production environments and considerations for scalability, reliability and maintainability
Ability to effectively communicate findings, insights, and results to both technical and non-technical stakeholders through presentations, reports and visualizations

More about this course

In this course you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system
You will learn about training data, and how to use a set of data to discover potentially predictive relationships
As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets
You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning

HarvardX: Data Science: Machine Learning
at
edX
Curriculum

The basics of machine learning

How to perform cross-validation to avoid overtraining

Several popular machine learning algorithms

How to build a recommendation system

What is regularization and why it is useful?

HarvardX: Data Science: Machine Learning
at
edX
Faculty details

Rafael Irizarry

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.