Data professionals may use machine learning techniques to help them execute their tasks. You can get a machine learning certification to gain new skills and show your mastery of standard industry procedures. Understanding what these certifications are and how they differ can help you select the right one for you and your career. In this article, we review what a machine learning certification is, the benefits of pursuing one, eight types of certifications and jobs that use machine learning.
What is a machine learning certification?
A machine learning certification is a certificate you earn as a data science professional that shows you have a strong theoretical foundation and understanding of complex computer algorithms. These certifications can show that you’re committed to professional development and have pursued training regarding the best practices related to machine learning. Many certification courses focus on career preparation by not only introducing the algorithms but also highlighting their practical use.
Benefits of getting a machine learning certification
There are several benefits to getting a machine learning certification, like improving your resume and helping you get noticed by potential employers. Certification may provide you with information about the latest developments in algorithms and processes and teach you about various theories, including game theory and information theory. These courses can help you learn advanced data science techniques and may allow you to apply for high-level positions and advance your career.
8 certifications for machine learning
Here’s a list of eight certifications for machine learning that you can consider:
1. IBM Machine Learning Professional Certificate
This machine learning certificate offered by IBM includes six courses that discuss the theoretical foundations of machine learning techniques and their practical application. You can take this course with or without a strong background in programming, which makes it accessible to a wide range of professionals. This program also includes information about certain machine learning programs you can use to develop and create your projects, allowing you to build your profile throughout the course.
2. Certificate in Machine Learning by Stanford
This 11-week course, offered by Stanford University, covers basic information necessary for the machine learning field, including math and statistics. Machine learning experts may be familiar with this well-established program, and they offer it in 10 languages. As the program progresses, professors may discuss the fundamentals of machine learning algorithms and their application in a variety of fields and professions, including computer vision, audio manipulation, database mining and medicine.
3. Professional Certificate Program in Machine Learning and Artificial Intelligence by MIT
This program, offered by MIT, can be great for professionals who are familiar with machine learning and industry newcomers. It provides current information relevant to the industries of machine learning and artificial intelligence, primarily focusing on algorithms and techniques in processing and dealing with big data. You also have the option to extend the program to highlight a particular area, including medicine, efficient deep learning or computer vision.
4. AWS Certified Machine Learning
This certificate, offered by Amazon, is more specific than other certifications. Its goal is to improve your ability to design, develop and implement machine learning by using models with the AWS Cloud. This program is available in English, Korean and Chinese to appeal to a wider audience of data professionals.
5. Machine Learning Certificate by Harvard
Harvard University offers this course-based certification program on machine learning that is also part of a bigger data science certificate they offer. In this course, you can receive an introduction to techniques and algorithms, learn how to use cross-validation and how to create a recommendation system. You can also gain access to some new and emergent algorithms that may further your professional skills and knowledge.
6. Professional Machine Learning Engineer by Google
To get this certificate from Google, they only require you to pass an exam rather than take an entire course. This test measures your skills and competencies with design solutions, data processing and developing machine learning models. You may also automate efficient pipelines and optimize your solutions to pass the exam. While you can take this test without enrolling in a course, there are materials available to help you prepare.
7. Machine Learning at Udacity
There are two modules for this course, which discuss supervised and unsupervised learning. Various companies use these two different modes of machine learning to execute operations, like voice recognition and to differentiate spam emails. This course also covers which programs you can use to imitate human learning and brain activity. This free, four-month course can be a great opportunity for experienced and beginning data professionals.
8. eCornell Machine Learning Certificate
This certificate, offered by Cornell University, prepares you to use various coding software as an aspect of machine learning with a three-and-a-half month-long course. You can explore how to create mental models to understand the approaches of various data scientists and learn from their programming techniques. You may also learn about implementation concepts, such as regression trees, to gain a better understanding of the practical application of algorithms. When gaining this certificate, you may also practice debugging and improving machine learning models.
What jobs use machine learning?
If you’re considering getting a certificate in machine learning to enter a career in this industry, there are several job options you can choose from, including:
Machine learning engineer
Machine learning engineers create algorithms and self-running programs to identify and decode patterns. These professionals analyze large amounts of data to control and predict results in software program operations to improve the program operations’ accuracy. Although a new and emergent field, machine learning engineers perform similar tasks as data scientists. Machine learning engineers may benefit from having certain skills, including:
An understanding of computer programming and coding languages
Modeling and data evaluation
Research and analysis
Software engineering and design
Deep learning engineer
Deep learning engineers are computer scientists who focus on using deep learning to execute programming tasks related to artificial intelligence (AI) technologies and operations. Their work specializes in creating software that imitates human brain functions to create realistic and innovative AI solutions. Their job duties can include creating system and program design plans and figuring out how to integrate programs and machines.
Data analysts transform complicated data into understandable information. They use data analysis tools to help clients scrutinize and identify trends. Their job duties can include extracting data, designing and developing algorithms and coding. Some of their other responsibilities include:
Performing analyses to determine the quality of the data
Performing analyses to understand the meaning of the data
Creating reports according to their analyses and presenting them to their clients
Removing corrupted data
Data scientists, like machine learning engineers, work with large data sets to solve complex problems. However, they use modeling, mathematical, programming and statistical skills to decode patterns and design data modeling processes and predictive models. This allows them to perform custom analyses for their clients and employers. Their predictive models can help their clients make objective business decisions based on the information the data scientists obtained.
Similar to other professions with machine learning certifications, data engineers work with data sets, although their responsibilities correspond to cleaning up and transporting complex information. They aggregate and organize data to relocate it to data warehouses. They connect data from one system to another so that data scientists can use it for their process and model designs.
I hope you find this article helpful.