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IBM Machine Learning Professional Certificate

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Course Features

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Duration

6 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

3 hours per week

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Teaching Type

Self Paced

Course Description

Machine learning is highly desirable for jobs related to modern AI applications. According to LinkedIn, this field has seen a 74% increase in hiring over the last four years. For those interested in pursuing a career as a Machine Learning professional, the IBM Professional Certificate is available. They can use the following types of Unsupervised Learning (Supervised Learning), Deep Learning, Reinforcement Learning, and Supervised Learning(Unsupervised Learning). You can add specific topics to your learning such as Survival Analysis or Time Series Analysis. This program contains 6 courses that will give you a solid theoretical understanding and extensive practice in the main algorithms. Follow the instructions to create your own projects using the best open-source libraries and frameworks. The intermediate series doesn't require programming experience. However, you will need to have a basic understanding about statistics and Python programming. This course is open to anyone who has a desire and the ability to learn. We start small, providing a solid theoretical foundation and code-along demos. Next, we will move onto more complex topics. To recognize your machine-learning proficiency, you will be awarded a Coursera Professional Certificate as well as a digital badge by IBM.

Course Overview

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud

Handle categorical and ordinal features, as well as missing values

Articulate why regularization may help prevent overfitting

Describe and use other ensemble methods for classification

Understand metrics relevant for characterizing clusters

Try clustering points where appropriate, compare the performance of per-cluster models

Identify types of problems suitable for survival analysis

Course Instructors

Mark J Grover

Digital Content Delivery Lead

Mark J. Grover is a member of the IBM Data & AI Learning team and specializes in creating and delivering online content. He comes to IBM from Cape Fear Community College in Wilmington, NC where he wa...

Miguel Maldonado

Machine Learning Curriculum Developer

Miguel Maldonado is the instructor for this course
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