Machine Learning Specialization
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Machine Learning Specialization
Build Intelligent Applications.
Master machine learning fundamentals in four hands-on courses.
225,125 already enrolled
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Get in-depth knowledge of a subject
from 16,271 reviews of courses in this program
Intermediate level
Some related experience required
2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
from 16,271 reviews of courses in this program
Intermediate level
Some related experience required
2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
Applied Learning Project
Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization. They will walk away with applied machine learning and Python programming experience.
Skills you'll gain
- AI Personalization
- Applied Machine Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Bayesian Statistics
- Data Mining
- Feature Engineering
- Image Analysis
- Logistic Regression
- Machine Learning
- Machine Learning Algorithms
- Machine Learning Methods
- Model Evaluation
- Model Training
- Predictive Modeling
- Regression Analysis
- Statistical Machine Learning
- Statistical Modeling
- Supervised Learning
- Unsupervised Learning
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Specialization - 4 course series
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
Applied Learning Project
Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization. They will walk away with applied machine learning and Python programming experience.
What you'll learn
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?
Skills you'll gain
Category: Model Evaluation Category: Machine Learning Category: Regression Analysis Category: Deep Learning Category: Transfer Learning Category: Computer Vision Category: Machine Learning Methods Category: Supervised Learning Category: Predictive Modeling Category: Machine Learning Algorithms Category: Feature Engineering Category: Model Deployment Category: Image Analysis Category: Application Development Category: Artificial Intelligence and Machine Learning (AI/ML) Category: Artificial Intelligence Category: Python Programming Category: Model Training Category: Applied Machine Learning Category: AI Personalization
What you'll learn
Case Study - Predicting Housing Prices
Skills you'll gain
Category: Regression Analysis Category: Model Evaluation Category: Model Optimization Category: Feature Engineering Category: Machine Learning Methods Category: Predictive Modeling Category: Supervised Learning Category: Statistical Machine Learning Category: Machine Learning Category: Machine Learning Algorithms Category: Statistical Modeling Category: Statistical Methods Category: Data Preprocessing Category: Model Training Category: Applied Machine Learning
What you'll learn
Case Studies: Analyzing Sentiment & Loan Default Prediction
Skills you'll gain
Category: Logistic Regression Category: Decision Tree Learning Category: Model Evaluation Category: Scalability Category: Machine Learning Algorithms Category: Model Optimization Category: Classification Algorithms Category: Supervised Learning Category: Model Training Category: Data Preprocessing Category: Algorithms Category: Machine Learning Category: Predictive Modeling Category: Applied Machine Learning Category: Natural Language Processing Category: Risking Category: Text Mining Category: Machine Learning Methods
What you'll learn
Case Studies: Finding Similar Documents
Skills you'll gain
Category: Unsupervised Learning Category: Scalability Category: Machine Learning Algorithms Category: Bayesian Statistics Category: Machine Learning Category: Statistical Modeling Category: Distributed Computing Category: Sampling (Statistics) Category: Machine Learning Methods Category: Applied Machine Learning Category: Text Mining Category: Statistical Machine Learning Category: Algorithms Category: Unstructured Data Category: Probability Distribution Category: Statistical Inference Category: Data Mining
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Instructors
University of Washington
6 Courses500,099 learners
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Frequently asked questions
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in about 8 months.
Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.
You should have some experience with computer programming; most assignments in this Specialization will use the Python programming language. This Specialization is designed specifically for scientists and software developers who want to expand their skills into data science and machine learning, but is appropriate for anyone with basic math and programming skills and an interest in deriving intelligence from data.
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Financial aid available,
¹ Median salary and job opening data are sourced from Lightcast™ Job Postings Report. Content Creator, Machine Learning Engineer and Salesforce Development Representative (1/1/2024 - 12/31/2024) All other job roles (6/1/2025 - 6/1/2026)