The Best Machine Learning Certifications for Your Career

Machine learning (ML) is a branch of artificial intelligence that enables applications and computer systems to learn, identify patterns, predict outcomes, and make decisions. Computers can perform all these activities with minimal human intervention.

Machine learning helps meet essential business needs, especially in working with huge amounts of data and making timely data-driven decisions. Embracing machine learning can give you a competitive advantage over your competitors since you are able to predict and make accurate choices. Machine learning also assists businesses in cutting costs and reducing errors.

The demand for machine learning engineers is on the rise. Companies are willing to pay more to acquire top talent. For instance, the 2021 median pay for computer and information research scientists is $131,490 per year.

You need the right skills and knowledge to get hired as a machine learning engineer.

Machine learning certification courses and exams can help you develop crucial skills and experience that you can showcase to hiring managers. Besides validating your expertise, certification exams and courses also expand your understanding of technical concepts and tools that you can use to develop machine learning applications that solve real-world problems.

Which certification program is right for you depends on your previous experience and career goals. For instance, if you’re a beginner, you may start with courses that teach you basic programming skills and then advance to learn about more complex aspects of machine learning. Experts, on the other hand, may wish to choose certification courses that expand on the knowledge they already have rather than beginning with the basics.

Here are the best machine learning certifications you should consider if you’re looking to grow your career:

Our top picks for machine learning certification programs

• IBM Machine Learning Professional Certificate
• Professional ML Engineer Certification
• MIT Professional Certificate Program in Machine Learning & Artificial Intelligence
• AWS Certified Machine Learning

Alternative machine learning programs

• Machine Learning Cornell Certificate Program
• Machine Learning Specialization By Stanford
• Machine Learning Bootcamp by Springboard
• Machine Learning with Python
• Harvard Data Science: Machine Learning
• TensorFlow Developer Certificate Program

Our top picks for machine learning certification programs

In this section, we’ll discuss some of the best certification programs that we believe could be valuable to you. The courses provide a good introduction to top machine learning platforms used across the world.

IBM Machine Learning Professional Certificate

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The IBM Machine Learning Professional Program helps learners acquire skills in reinforcement learning, deep learning, supervised learning, and unsupervised learning.

Though having some knowledge of linear algebra, statistics, and Python programming can put you ahead of the game when obtaining this certification, it’s still suitable for individuals with only basic computer skills.

The IBM Machine Learning Professional Program is made up of the following six courses:

• Exploratory Data Analysis for Machine Learning. This course helps you learn data analysis basics, including retrieving data from sources such as APIs, SQL databases, and cloud platforms. It also introduces data cleaning techniques used to detect and eliminate outliers and handle missing values.
• Supervised Machine Learning: Regression. This course teaches you how to train regression models to make accurate predictions.
• Supervised Machine Learning: Classification. This course teaches you how to train predictive models to classify different objects. You are also introduced to related machine learning concepts such as decision trees, ensemble methods, and logistic regression.
• Unsupervised Machine Learning. This course helps you understand problems you can solve with unsupervised machine learning methods, dimension reduction, and clustering algorithms.
• Deep Learning and Reinforcement Learning. This course introduces you to concepts such as neural networks, modern deep learning architectures, and the dimensionality curse. It also highlights some problems deep learning and reinforcement learning can help address.
• Machine Learning Capstone. In this course, learners build a recommendation system using popular Python frameworks such as TensorFlow/Keras, scikit-learn, and Pandas.

Benefits

• You’ll be able to acquire hands-on experience by developing real-world programs such as recommender systems, as well as training neural networks, classification, and regression models.
• The course introduces you to other machine learning students you can collaborate with to build machine learning applications. Having these connections can be really helpful while you learn the material too.
• All courses are available online. This means you can learn on your own schedule.
• Once you complete the course, you earn an IBM Machine Learning Professional Certificate from Coursera and a digital recognition badge from IBM that you can show to recruiters.

Here are some other things you may need to know about the IBM Machine Learning Professional Certificate:

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Professional ML Engineer Certification

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The Professional ML Engineer Certification offered by Google Cloud allows an ML engineer to demonstrate proficiency in areas such as metrics interpretation, data pipeline, and model architecture.

Specifically, the exam demonstrates your ability to identify problems, develop solutions, create machine learning models, automate ML pipelines, design data processing systems, and maintain ML solutions.

Below are some of the benefits of the Professional ML Engineer Certification:

• The exam helps you validate your ML skills and knowledge. Keep in mind, the certificate is only valid for two years, and you’ll need to recertify once the period expires to maintain your certification status.
• While you obtain the certification, you can learn valuable tips and insights from other certified experts to help you grow your career.
• Google shapes your learning path by providing online training, machine learning tools, and resources.

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MIT Professional Certificate Program in Machine Learning & Artificial Intelligence

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MIT Professional Certificate Program in Machine Learning & Artificial Intelligence helps you develop data analysis skills and master ML algorithms, models, and tools. It will also help you explore approaches such as reinforcement learning, deep learning, and computer vision.

You can earn a professional certificate from MIT by completing two compulsory machine learning courses and three elective ones. The program contains the following courses:

• Machine Learning for Big Data and Text Processing: Foundations (compulsory). Completing this course will ensure that you’re familiar with machine learning theories and mathematical concepts. It will also help you understand ML concepts such as regression, classification, optimization, and statistics. The course takes about two days to complete.
• Machine Learning for Big Data and Text Processing: Advanced (compulsory). This course highlights the different problems that modern ML applications and predictive algorithms can solve. This course takes about three days.
• AI for Computational Design and Manufacturing. This course introduces the learner to computational design including complex manufacturing hardware, design workflows, and digital materials. The course takes about five days to complete.
• Advanced Reinforcement Learning. The course dives deep into the theories and models of Reinforcement Learning. It takes about two days to complete.
• Applied Data Science Program. This course helps improve your data analysis skills by introducing you to concepts such as computer vision, regression, neural networks, recommendation engines, supervised and unsupervised learning, and time series analysis. The Applied Data Science program takes 5 days to complete.
• AI Strategies and RoadMap. In this course, you learn the strategies and skills for deploying machine learning applications and other digital products. This course takes five days.

Benefits

• MIT offers in-person learning, which allows you to meet and network with other ML experts. You can also attend the ML classes virtually which is convenient for those with busy schedules or who live far away.
• The program helps you understand how AI and ML impact the modern workplace.
• While obtaining this certification, you’ll acquire essential skills and knowledge for creating effective ML applications.
• As an alumnus, you’ll receive a 15% discount on future MIT machine learning courses. You’ll also earn Continuing Education Units (CEUs).
• You’ll become a member of the MIT Professional Education LinkedIn group, establishing connections that can help make a positive impact on your career.

Here are more details regarding the MIT Machine Learning Certification Program:

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AWS Certified Machine Learning

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AWS Certified Machine Learning is a professional certification exam offered by Amazon Web Services that validates your experience in building, training, and deploying machine learning applications and models.

To pass the exam, candidates must demonstrate their ability to follow model-training, deployment, and operational best practices. They should also know how to create ML algorithms and perform hyperparameter optimization.

The scoring range for this certification exam is between 100 and 1,000. You must attain at least 750 to pass the exam.

Benefits

• AWS Certified Machine Learning helps you validate your knowledge of machine learning concepts such as modeling, exploratory data analysis, and data engineering.
• Amazon provides manuals and resources you can use to study for the exam and boost your knowledge of ML concepts.
• You’ll receive an AWS-certified ML badge that you can use on your resume.

Below are more details regarding the AWS Certified Machine Learning exam:

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Alternative machine learning programs

This section discusses alternative certification programs that can equip you with vital ML skills and shape your career path.

Machine Learning Cornell Certificate Program

Cornell’s Machine Learning Certificate Program helps learners understand how data scientists develop solutions for different real-world problems using machine learning.

The program also introduces students to ML algorithms such as k-nearest neighbors, regression trees, naive Bayes, and more. Furthermore, it highlights different strategies that can be used for debugging and improving ML models.

Cornell’s Machine Learning Program consists of the following courses:

• Problem-Solving with Machine Learning. This course highlights different machine learning problems and how you can investigate, evaluate, and resolve them using ML algorithms.
• Linear with Linear Classifiers. This course introduces learners to the Perceptron algorithm, which is used for logistic and linear regression.
• Estimating Probability Distributions. This course focuses on the Bayes Optimal Classifier and teaches you how assumptions can affect ML estimations.
• Decision Trees and Model Selection. This course covers the classification and regression tree algorithm. It also explains how an algorithm’s  hyperparameters can affect the accuracy of a model.
• Debugging and Improving Machine Learning Models. This class teaches you how to identify and eliminate bugs in your machine learning code.
• Learning with Kernel Machines. This course explores support-vector machines and margin classifiers.
• Deep Learning and Neural Networks. This course helps you build and train a neural network using PyTorch.

Benefits

• Through these courses, you’ll gain hands-on experience by creating numerous ML applications like image classifiers and recognition systems.
• The program improves your linear algebra skills, which are essential in machine learning.
• Students can learn how to train neural networks and deploy ML models in actual production environments.
• The program is 100% online, which means you can learn on your own schedule.
• Cornell’s Machine Learning course has a pretest session that helps you assess your readiness to dive into the program.

Below are some more details regarding Cornell’s Machine Learning Program:

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Machine Learning Specialization By Stanford

The Machine Learning Specialization Program introduces beginners to machine learning fundamentals like unsupervised learning, neural networks, logistic regression, multiple linear regression, clustering, natural language processing, and dimensionality reduction. Stanford offers this certification program in collaboration with DeepLearning AI.

The Machine Learning Specialization program consists of the following three courses:

• Supervised Machine Learning: Regression and Classification. This course teaches you how to build machine learning models using Python, scikit-learn, and NumPy libraries.
• Advanced Learning Algorithms. In this course, you’ll build a neural network using TensorFlow. You’ll also use tree ensemble methods such as boosted trees and random forests to create other real-world AI applications.
• Unsupervised Learning, Recommenders, Reinforcement Learning. This course helps you build recommender systems using supervised and unsupervised learning techniques.

Benefits

• The program is offered online, which means you can make progress at your own pace.
• Stanford’s Machine Learning Specialization program introduces beginners to basic Python programming before advancing them to more complex concepts. The natural progression of the courses makes the program perfect for beginner learners as they build an understanding of machine learning.
• Learners are awarded a certificate on the successful completion of the program. This is a valuable certificate for a beginner to bring to the job market.

The table below provides more information on Stanford’s certification program:

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Machine Learning Bootcamp by Springboard

With Springboard’s Machine Learning Bootcamp, you can acquire essential machine learning skills in areas such as data cleaning and transformation, anomaly detection, and linear and logistical regression.

Students can also design machine learning systems and build applications that can be accessed via APIs or other mediums.

The bootcamp is divided into the following modules:

• Machine Learning Techniques and Models
• Deep Learning
• Computer Vision and Image Processing
• ML Models at Scale and in Production
• Deploying ML Systems to Production
• Working with Data

Benefits

• The program is 100% online, which means you can learn on your own schedule.
• The bootcamp uses a project-based learning style that allows learners to acquire hands-on experience.
• You can meet with mentors and industry experts who advise on best practices to help you succeed in a machine learning career.

Here are the specifications for Springboard’s Machine Learning Bootcamp.

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Machine Learning with Python

Machine Learning with Python is a Coursera certification program offered by IBM. It seeks to equip learners with skills in machine learning concepts such as hierarchical clustering, classification, regression, SciPy, and scikit-learn. It also teaches linear classification methods like support vector machines and multiclass prediction.

The Machine Learning with Python program is made up of the following modules:

• Introduction to Machine Learning
• Regression
• Linear Classification
• Clustering
• Capstone Project

Benefits

• The course is 100% online, which means you can learn on your own schedule.
• You’re awarded a certificate that you can share with recruiters upon course completion.
• You can set flexible deadlines according to your schedule.

Here’s a summary of the Machine Learning with Python program:

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Harvard Data Science: Machine Learning

Like other ML programs, Harvard’s Data Science: Machine Learning Certification Program introduces students to ML algorithms, cross-validation, and other data science methodologies. The course also guides learners in building real-world applications, such as image classifiers and recommender systems.

The Machine Learning Certification Program is made up of the following modules:

• Basics of Machine Learning
• Cross-validation
• Popular Machine Learning Algorithms
• Building a Recommendation System
• Regularization

Benefits

• The course is 100% online, which means you can learn on your own schedule.
• It’s free for a limited period.
• You’re awarded a certificate that you can share with recruiters upon course completion.

Here are more details regarding Harvard’s Machine Learning Program:

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TensorFlow Developer Certificate Program

The TensorFlow Developer Certificate provides an opportunity for individuals to showcase their skills, knowledge, and expertise. Developers are given an assessment exam, and those who pass can join the TensorFlow Certificate Network.

Benefits

• Upon successful completion, the TensorFlow assessment exam demonstrates your proficiency in machine learning.
• Joining the TensorFlow Certificate Network can increase your visibility to hiring companies. You can also share the certificate on social platforms such as LinkedIn, Twitter, GitHub, and Facebook.
• You can collaborate with other certificate holders to complete different ML projects.

Here are other things you may need to know about the TensorFlow Developer Certificate Program:

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Find the right program for you

Enrolling in a professional certification program can help build your skills and equip you with relevant hands-on experience in machine learning. Often, taking part in these courses can also grant you valuable access to resources that can improve your resume. Recruiters look at certification as demonstrating knowledge and proficiency in the subject matter.

Choosing a certification program to join, however, can sometimes seem overwhelming. You’ll have to comb through numerous programs, look at course modules and objectives, and determine whether or not the course objectives fit your goals.

Upwork connects you to freelance machine learning engineers who can help you find the right certificate program.

If you’re a machine learning expert looking for work, you can get started by selling your services and meeting potential clients on Upwork today.

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation. Prices are current at the time of writing and may change over time based on each service’s offerings.