5 Popular Machine Learning Certifications: Your 2023 Guide

Written by Coursera • Updated on

Whether you’re just beginning a career or are already a practicing professional, a machine learning certification or certificate can help you get to the next level.

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When applying for a programming or data science job, machine learning certifications and certificates have the potential to help you stand out from the crowded pool of candidates. Whether you’ve just completed a course of study or passed an exam offered by a respected institution, obtaining a certificate or certification is a real accomplishment that indicates your knowledge, experience, and expertise in the field of machine learning. 

But, what certificates and certifications are right for you? In this article, you’ll learn more about the difference between certificates and certifications and explore five of the most popular ones for machine learning available today. 

Can a machine learning certificate help you? Certifications vs. certificates 

Though they are often confused, certificates and certifications are not the same. Certificates are awarded to you for completing a specific course of study, such as an online or in-person educational program, and effectively indicate your training in a specific subject. Certifications, meanwhile, are awarded to you for completing a specific exam, such as Google’s Professional Machine Learning Engineer certification, and indicate your expertise in a specific subject by passing a particular test.  

Both certificates and certifications have the potential to help you stand out from the crowd when applying for a position. While a certificate emphasizes your training and educational accomplishments, certifications indicate your dedication to professional development and your specialized skill set. 

5 Machine learning certifications and certificates 

Machine learning certifications and certificates can be advantageous when applying for a job. In this list, you’ll find five popular certificates and certifications that you might consider exploring as you develop your machine learning skills. 

1. Andrew Ng’s Machine Learning Specialization 

AI visionary Andrew Ng’s Machine Learning Specialization is an online, three-course educational program designed to help course takers master fundamental AI concepts and develop practical machine learning skills, such as building and training machine learning models.

The highly-regarded specialization is offered jointly by Stanford University and DeepLearning.AI, and is specifically designed for beginners as well as more advanced course takers. Upon completing the specialization, you will receive a shareable certificate that can be cited on your resume to demonstrate your knowledge and skills to potential employers. 

Requirements: The course is suitable for beginners with knowledge of basic coding and high school-level math concepts.

Cost: The course costs $49 per month by subscription to Coursera. 

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specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

4.9

(7,641 ratings)

127,121 already enrolled

BEGINNER level

Average time: 3 month(s)

Learn at your own pace

Skills you'll build:

Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensembles, Advice for Model Development, Collaborative Filtering, Unsupervised Learning, Reinforcement Learning, Anomaly Detection

2. IBM Machine Learning Professional Certificate 

IBM’s Machine Learning Professional Certificate is an online, six-course educational program that equips course takers with practical machine learning skills, such as supervised learning, unsupervised learning, and deep learning. At the same time, the program also introduces course takers to such specialized topics as time series analysis and survival analysis. 

Upon completion of the program's six courses, you will be awarded a professional certificate from IBM and Coursera that indicates you have completed the course of study. This certificate can be cited on your resume to indicate your knowledge and skill set to potential employers. 

Requirements: There are no prerequisites to take the course, but IBM suggests that you possess some related experience and are at an intermediate knowledge level. 

Cost: The course costs $39 per month by subscription to Coursera. 

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professional certificate

IBM Machine Learning

Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

4.6

(995 ratings)

7,674 already enrolled

INTERMEDIATE level

Average time: 9 month(s)

Learn at your own pace

Skills you'll build:

Artificial Intelligence (AI), Machine Learning, Feature Engineering, Statistical Hypothesis Testing, Exploratory Data Analysis, Regression Analysis, Supervised Learning, Linear Regression, Ridge Regression, Machine Learning (ML) Algorithms, Decision Tree, Ensemble Learning, Classification Algorithms, Dimensionality Reduction, Unsupervised Learning, Cluster Analysis, K Means Clustering, Principal Component Analysis (PCA), Deep Learning, Artificial Neural Network, Reinforcement Learning, keras, Python Programming, Data Analysis, unsupervised machine learning

3. AWS Certified Machine Learning - Specialty 

Amazon Web Service’s (AWS) Certified Machine Learning - Speciality Certification indicates your expertise in building, training, and running machine learning models in AWS. 

To earn the certification, you must take and pass a 180-minute exam consisting of 65 multiple-choice and response questions. Designed for professional developers and data scientists, the exam tests your understanding of machine learning algorithms, ability to implement hyperparameter optimization, and perform best practices when training, deploying, and operating machine learning models. 

Requirements: The exam is designed for those with at least one year of hands-on experience developing, running, or architecting ML and deep learning workloads on AWS. 

 

Cost: $300 (plus tax) 

Prepare for AWS with AWS

Prepare for the exam by taking a course designed by AWS themselves on Coursera.  In AWS’ Introduction to Machine Learning on AWS, you’ll explore the services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents.

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4. Google Professional Machine Learning Engineer Certification 

Google’s Professional Machine Learning Engineer Certification indicates your expertise in designing, building, and productionizing machine learning models using Google Cloud and industry-proven techniques. To earn the certification, you must take and pass a two hour exam consisting of 50-60 multiple choice questions covering such topics as framing ML problems, architecting ML solutions, and developing ML models.   Certifications are valid for two years, after which holders must recertify to maintain certification. 

Requirements: There are no formal requirements to take the exam but Google recommends that test takers have three or more years of industry experience and at least one year of experience designing and managing machine learning solutions in Google cloud. 

Cost: $200 (plus tax) 

Get ready with Google

Google’s Professional Machine Learning Engineer certification will signal your expertise in crafting and implementing machine learning solutions with google cloud to potential employers and clients. What better way to prepare for the exam than by taking a course offered by Google? 

In Google Cloud’s Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate, you’ll explore how to design, build, and productionalize ML models to solve business challenges using Google Cloud technologies.

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5. University of Washington Machine Learning Specialization 

The University of Washington’s Machine Learning Specialization is a four-course, online educational program covering the major areas of machine learning, including prediction, classification, clustering, and information retrieval. Through the course, you’ll also analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. 

Upon completion of the specialization, you will receive a shareable certificate that you can cite on your resume to signal your knowledge and skill set to potential employers. 

Requirements: The specialization has no prerequisites but is recommended for those with an intermediate knowledge of the field who possess some related experience.  

Cost: The course costs $49 per month by subscription to Coursera.

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specialization

Machine Learning

Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses.

4.7

(12,347 ratings)

191,031 already enrolled

INTERMEDIATE level

Average time: 7 month(s)

Learn at your own pace

Skills you'll build:

Data Clustering Algorithms, Machine Learning, Classification Algorithms, Decision Tree, Python Programming, Machine Learning Concepts, Deep Learning, Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis, Logistic Regression, Statistical Classification, K-Means Clustering, K-D Tree

Get certified with Coursera 

Certificates and certifications can signal your knowledge, skill set, and dedication to the field of machine learning to potential employers. If you’re looking to obtain a certificate or pursue a professional certification, then you might consider taking a cost-effective, flexible course of study through Coursera to help you do it. 

In Andrew Ng’s beginner-friendly Machine Learning Specialization, you’ll master key concepts and gain the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, then Ng’s recently updated Machine Learning Specialization is the best place to start.

Placeholder

specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

4.9

(7,641 ratings)

127,121 already enrolled

BEGINNER level

Average time: 3 month(s)

Learn at your own pace

Skills you'll build:

Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensembles, Advice for Model Development, Collaborative Filtering, Unsupervised Learning, Reinforcement Learning, Anomaly Detection

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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