- Deep Learning
- Python Programming
- Unsupervised Learning
- Supervised Learning
- hyperparameter tuning
- Hyperparameter
- Decision Tree
- ensembling
- sklearn
- Dimensionality Reduction
- Cluster Analysis
- Recommender Systems
Machine Learning: Theory and Hands-on Practice with Python Specialization
Develop Foundational Machine Learning Skills. Add Supervised, Unsupervised, and Deep Learning techniques to your Data Science toolkit.
Offered By
What you will learn
Explore several classic Supervised and Unsupervised Learning algorithms and introductory Deep Learning topics.
Build and evaluate Machine Learning models utilizing popular Python libraries and compare each algorithm’s strengths and weaknesses.
Explain which Machine Learning models would be best to apply to a Machine Learning task based on the data’s properties.
Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization.
Skills you will gain
About this Specialization
Applied Learning Project
In this specialization, you will build a movie recommendation system, identify cancer types based on RNA sequences, utilize CNNs for digital pathology, practice NLP techniques on disaster tweets, and even generate your images of dogs with GANs. You will complete a final supervised, unsupervised, and deep learning project to demonstrate course mastery.
Calculus, Linear algebra, Python
Calculus, Linear algebra, Python
How the Specialization Works
Take Courses
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Hands-on Project
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

There are 3 Courses in this Specialization
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
Unsupervised Algorithms in Machine Learning
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.
Introduction to Deep Learning
Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs.
Offered by

University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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