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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

Courses

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

This specialization can be taken for academic credit as part of CU Â鶹ӰԺ’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: and .Ìý