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The world of machine learning certification is a confusing one. There are countless online options that promise a certificate for taking a course or passing an exam. There is a vast variety in the quality of these certifications and how much credibility they carry with employers. Let’s go through five of the best options today for certifications that will really prove your knowledge to your next boss.

Why Get an Artificial Intelligence Certification?

If you’re looking for a job in machine learning, you’ll want to make your resume or CV as attractive as possible, and a certification can be part of that. Different employers and different cultures place different priorities on certifications. Many US-based companies will put a lot more weight on your professional experience and formal education. But in other countries, certification can be much more important. For example, many students who take my machine learning certification prep course live in India. How much your certification helps may also depend on whether your hiring manager understands the knowledge and value that comes with certification.

But the best certifications aren’t easy. Even if you are already experienced in machine learning, expect to spend about a month preparing for the exam and several hours taking the exam itself. You should also prepare to spend a few hundred dollars on exam fees and study materials. 

But in short, getting certified in machine learning can’t hurt. It might help, and you will probably become a better machine learning engineer just from all the learning and preparation you need to do. These exams test a broad range of real-world scenarios and problems. While studying, you’ll probably learn about new ones you have never encountered before.

1. AWS Certified Machine Learning Specialty

Big tech companies like Amazon, Microsoft, and Google offer many of the most-respected machine learning certifications. While these certifications come with undeniable name recognition and authority, they do tend to focus on technologies offered by the company hosting the exam. Therefore, you need to do your homework and identify which cloud platform your potential employers are using. Most likely, it’s Amazon Web Services (AWS), which is why the AWS Certified Machine Learning Specialty exam is on the top of this list.

Taking this exam, myself has changed my mind about how valuable certifications can be. As a former Amazon hiring manager, I didn’t put much weight on somebody’s ability to pass an exam. But passing this AWS exam really does require the ability to apply a wide range of tools and experience to real-world problems. Seeing an AWS Certified Machine Learning Specialty certification on a résumé today tells a hiring manager you have what it takes to succeed.

To pass the AWS Certified Machine Learning Specialty exam, you do need broad and deep knowledge of AWS. But the exam also tests your general knowledge of machine learning and artificial intelligence (AI), the nuances of training these systems, and feature engineering. It’s not just an AWS exam, and that makes it even more valuable.

This 3-hour exam costs 300 USD and covers the following domains:

  • Data engineering: This includes storing data using S3, ingesting data using Kinesis and EMR, and transforming data using Glue, EMR, and AWS Batch. The exam also covers non-AWS tools, including Hadoop, Spark, and Hive.
  • Exploratory data analysis: This is less specific to AWS and tests your general knowledge on cleaning data, labeling data, feature engineering, analyzing data, and visualizing data.
  • Modeling: This is the largest part of the exam and is also not specific to AWS. You’ll need to understand how to choose an algorithm for a given business problem, the nuances of training the algorithm and the choices of computer hardware to do so, how to optimize your algorithm’s hyper-parameters, and how to evaluate the algorithm’s results.
  • Machine Learning Implementation and Operations: This gets back into AWS-specific territory. You’ll test your knowledge of how to scale machine learning (ML) systems across the cloud securely, as well as your knowledge of Amazon’s variety of higher-level ML services.

2. Google Professional Machine Learning Engineer

This certification is like the AWS certification, but it focuses instead on Google’s cloud platform. Like the AWS certification, the Google Professional Machine Learning Engineer certification comprises multiple-choice or multiple selection questions. Some of the exam questions are specific to Google’s platform, but many are not. Like Amazon, Google is a trusted name, and employers usually recognize this certification as reputable.

The Professional Machine Learning Engineer certification exam is shorter and cheaper (200 USD) than the AWS exam. The topics covered on the two-hour exam include:

  • Framing ML Problems: Choosing the right solutions for given business problems, what to output, and what data sources to use. Also, recognizing when ML is likely to fail and may not be the right solution.
  • Architecting ML Solutions: This covers building scalable and reliable ML solutions, including your choice of hardware, feature engineering, automation, monitoring, and security.
  • Designing Data Preparation and Processing Systems: This gets into data engineering — exploring your data, building data pipelines, and more depth into feature engineering.
  • Developing ML Models: This explores the details of specific machine learning and deep-learning models, along with the nuances of tuning, training, and scaling them.
  • Automating and Orchestrating ML Pipelines: This tests your ability to combine all the pieces of an ML system.
  • Monitoring, Optimizing, and Maintaining ML solutions: This delves more into the operations side of things and into ongoing tuning, training, and simplification of your models.

This exam focuses on building real-world systems at a massive scale. It also covers the challenges of maintaining and tuning those systems. Simply preparing for this exam will teach you a lot about how Google addresses these challenges and also make you a better engineer.

3. Microsoft Certified: Azure AI Fundamentals

If your current or prospective employer is a Microsoft shop, then they may be using Azure for the cloud computing resources ML requires. If so, the Microsoft Azure AI Fundamentals certification exam may be the right one for you. Like the AWS exam, it is specific to its own cloud platform but also tests your general knowledge of machine learning, artificial intelligence, and data engineering.

While the AWS Certified Machine Learning Specialty exam is for experienced technologists and covers advanced material, the Azure AI Fundamentals is for beginners. The exam is 60 minutes and contains only multiple-choice questions. The Azure AI Fundamentals certification will tell an employer that you know how to apply Azure’s services to machine-learning problems. It doesn’t show that you are a machine learning expert.

The cost of the exam depends on the country you’re in. It tests whether you can:

  • Describe Artificial Intelligence Workloads and Considerations: Besides testing your ability to move data and build larger systems to automate machine learning, this also touches on the ethics of AI, security, and privacy.
  • Describe Fundamental Principles of Machine Learning on Azure: This touches on some more general topics, such as choosing the right machine learning algorithms, evaluating your algorithms, and feature engineering.
  • Describe Features of Computer Vision Workloads on Azure: This tests the basics of when to use image classification, object detection, character recognition, facial recognition, computer vision, and more.
  • Describe Features of Natural Language Processing (NLP) Workloads on Azure: This covers Azure’s text analytics, language understanding, speed, and translator text services to solve problems in NLP.
  • Describe Features of Conversational AI Workloads on Azure: This domain tests your ability to build a chatbot using Azure.

4. IBM AI Enterprise Workflow V1 Data Science Specialist

These six professional courses are designed to prepare you for the IBM AI Enterprise Workflow V1 Data Science Specialist Expert Certification Exam. IBM AI Enterprise Workflow is a comprehensive end-to-end process that enables data scientists to create artificial intelligence solutions, starting with business priorities and working to introduce artificial intelligence into production. The learning aims to improve the skills of practical data scientists by clearly linking business priorities with technical implementation, linking machine learning with specialized AI use cases (such as visual recognition and NLP), and linking Python with technology . From IBM Cloud. The videos, reading materials, and case studies in these courses are designed to guide you through your job as a data scientist at a hypothetical broadcast media company.

Throughout the professionalization process, the focus will be on the data science practices of large modern companies. You will be guided through the use of enterprise-level tools on IBM Cloud, which will be used to create, implement, and test machine learning models. Your favorite open source tools, such as Jupyter notebooks and Python libraries, will be widely used for data preparation and modeling. These models will be deployed on the IBM Cloud using IBM Watson tools that work seamlessly with open source tools. After successfully completing this specialization course, you will be ready to take the official IBM certification exam for IBM AI Enterprise Workflow.

  • Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI
  • Section 2: Applications of Data Science and AI in Business
  • Section 3: Data understanding techniques in Data Science and AI
  • Section 4: Data preparation techniques in Data Science and AI
  • Section 5: Application of Data Science and AI techniques and models
  • Section 6: Evaluation of AI models
  • Section 7: Deployment of AI models
  • Section 8: Technology Stack for Data Science and AI

5. CertNexus Certified Artificial Intelligence Practitioner

The skills covered in this certification focus on three areas: software development, applied mathematics and statistics, and business analysis. The target students of CertNexus Certified Artificial Intelligence Practitioner certification may be performing well in one or two of these fields and would like to improve their skills in other fields so that they can apply artificial intelligence (AI) systems, especially machine learning models, to commercial problems.

Therefore, the target learner may be a programmer who wants to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying mathematics and statistics to business problems, but wants to develop machine-related technical skills. Teacher learning. A typical student in this Certified Artificial Intelligence Practitioner should have many years of computer technology experience, including some talents in computer programming.

Artificial intelligence (AI) and machine learning (ML) have become an important part of many organizational toolkits. When used properly, these tools can provide practical insights, drive critical decisions, and enable organizations to create exciting, novel, and innovative products and services. This course shows you how to apply various methods and algorithms to solve business problems through AI and ML, follow a structured workflow to develop powerful solutions, and use out-of-the-box open-source tools to develop, test, and implement these solutions, And make sure they protect the privacy of users. This course includes practical activities in each subject area. A detailed outline of the certification's syllabus is given below:

  • Specify a common method for applying AI and ML to solve specific business problems.
  • Collect and optimize data sets to prepare for training and testing.
  • Train and adjust machine learning models.
  • Finalize the machine learning model and present the results to the appropriate audience.
  • Build a linear regression model.
  • Build a classification model.
  • Create a clustering model.
  • Build decision trees and random forests.
  • Build a support vector machine (SVM).
  • Build an artificial neural network (ANN).
  • Promote data privacy and ethical practices in AI and ML projects.

Conclusion

Here, we have listed the best AI certification. Artificial Intelligence is rapidly occupying the world. Its technology has been used in games and personal home maintenance. Smart vehicles and drones are already being tested and implemented. It's time to master this incredible skill and immerse yourself in the revolution. Today, you can be the one who creates innovative technologies that will immerse mankind in the future.


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