In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning with supervised learning, you will review linear and logistic regression, KNN, decision trees, ensembling methods, and kernel methods. Next, you will review unsupervised methods, clustering, and recommender systems. And finally, you will close out the specialization with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries, and hands-on examples of CNNs and RNNs.

By completing this specialization, you will be able to:

  • Explore several classic supervised and unsupervised learning algorithms and introductory deep Learning topics
  • Explain which machine learning models are best applied to machine learning tasks based on the data’s properties
  • Build and evaluate machine learning models utilizing popular Python libraries and compare each algorithm’s strengths and weaknesses
  • Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization


  • Introduction to Machine Learning: Supervised Learning
  • Unsupervised Algorithms in Machine Learning
  • Introduction to Deep Learning

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program.

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