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Explore AI and Machine Learning. Go further with your Python skills while exploring the transformative fields of artificial intelligence, machine learning and deep learning.
Instructors: Victor Preciado
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(21 reviews)
Recommended experience
Intermediate level
Familiarity with Python programming, such as completion of Penn's Introduction to Programming with Python and Java Specialization.
(21 reviews)
Recommended experience
Intermediate level
Familiarity with Python programming, such as completion of Penn's Introduction to Programming with Python and Java Specialization.
Understand the philosophical, scientific, and historical foundations of artificial intelligence and machine learning.
Apply Python programming concepts to implement AI algorithms, machine learning models, and deep learning architectures.
Use essential statistics concepts, like probability, linear algebra and statistical learning theory, to develop and analyze machine learning models.
Implement and compare machine learning algorithms, such as linear regression and logistic regression, for both regression and classification tasks.
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This specialization will prepare learners to enter the exciting fields of artificial intelligence (AI) and machine learning. Across four courses, learners will familiarize themselves with AI, machine learning and deep learning essentials, while also gaining experience with statistics–the backbone of any machine learning problem. Learners will use Python to write programs that accomplish basic AI and machine learning tasks. By the end of this specialization, learners will be able to confidently use and discuss AI and machine learning tools and concepts, improve their Python skills, and be primed for more advanced study in these fields.
Applied Learning Project
Learners will complete Python coding assignments to solve common AI and Machine Learning tasks, such as uninformed/informed search and linear regression problems. In addition, learners will solve statistics assignments, building skills needed to address nearly every data science problem.
Understand the history and context of artificial intelligence through the lenses of philosophy and science fiction.
Explore different kinds of common search algorithms like A* Search, depth first search, breadth first search and more.
Comprehensively review probability and understand its role as a building block of data science.
Apply the central limit theorem, confidence intervals and the method of maximum likelihood to solving data science problems.
Review probability basics and understand essential theoretical framework to analyze statistical learning problems.
Use linear regression and Python programming to solve machine learning problems.
Understand the history and context of the deep learning field, and explore what "intelligence" really means.
Explore deep learning models like the perceptron, neural networks and backpropagation, and study the techniques that drive them.
Code a project using Python where you will preprocess data and use your data to train a Support Vector Machine (SVM.)
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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Learners should have Python programming skills. We recommend completing the Introduction to Programming with Python and Java Specialization or having similar knowledge. Basic Python will be reviewed in the first course of the specialization.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.