Mastering Machine Learning: Top 4 Certifications to Pursue in 2024

Walter Code
4 min readJan 25, 2024

Machine learning (further in the text, ML) is a branch of artificial intelligence (further in the text, AI) that empowers computers to learn from data and improve their performance on a task without explicit programming.

It encompasses a range of algorithms and techniques designed to enable systems to automatically recognize patterns, make predictions, and adapt to changing environments.

In this blog post, we bring you insights into four popular machine learning certificates offered by AWS, Google, IBM, and Microsoft, outlining what to expect from each, along with essential skills and tips to enhance your career in 2024.

Why is a machine learning certificate important?

Securing an ML certificate not only signifies mastery of essential skills but also serves as tangible proof of your proficiency, significantly boosting your chances of getting hired as a Machine learning engineer or specialist, particularly in a job market where the demand for such expertise continues to rise.

Data scientists, data analysts, software developers, and others working in related fields will find the skills obtained from these programs useful as well.

Given how AI and ML have exploded in use and popularity over the last few years, these technologies are showing up throughout IT environments. AI and ML skills won’t become obsolete anytime soon, and ML industry experts will continue to be needed.

Certifications provide the following advantages:

  • Proof of an IT professional’s expertise.
  • Hands-on experience dealing with real-world problems using machine learning tools and data sets that emulate real-world experiences.
  • Up-to-date insight and skills for an ever-changing field because ML certification courses are regularly updated.

AWS Certified Machine Learning — Specialty

The AWS Certified Machine Learning — Specialty exam evaluates proficiency in four key areas:

  1. Data engineering, involving the creation of ML-focused data repositories, and the implementation of data ingestion and transformation solutions.
  2. Exploratory data analysis encompasses tasks like data sanitization, preparation for modeling, feature engineering, and data analysis and visualization for ML.
  3. Modeling, which includes framing business challenges as ML problems, model selection, evaluation, and hyperparameter optimization.
  4. Machine learning implementation and operations, covering aspects like building ML solutions for performance, scalability, and fault tolerance, recommending and implementing suitable ML services, applying AWS security practices, and deploying and operationalizing ML solutions.

Exam preparation should include familiarity with topics such as data ingestion, transformation, cleaning, visualization, translating business problems into ML challenges, model training, and implementing ML services within the AWS environment. To excel in the exam, candidates are advised to have a minimum of two years of experience in developing and running machine learning workloads on AWS.

Google Cloud Professional Machine Learning Engineer

The Google Cloud Professional Machine Learning Engineer certification encompasses proficiency in six key domains:

  • Architecting low-code ML solutions.
  • Collaborating within and across teams for effective data and model management.
  • Scaling prototypes into robust ML models.
  • Serving and scaling ML models efficiently.
  • Automating and orchestrating ML pipelines.
  • Monitoring ML solutions for optimal performance.

To excel in the certification exam, Google recommends a minimum of three years of hands-on experience with its cloud platform. The recommended learning path for this certification covers fundamental topics such as big data and machine learning, progressing into specific areas like Google’s ML platform, TensorFlow, machine learning operations, and ML pipelines. This comprehensive approach ensures a well-rounded understanding of machine learning on the Google Cloud platform.

IBM Machine Learning Professional Certificate

The IBM Machine Learning Professional Certificate program focuses on four key domains: reinforcement learning, deep learning, supervised learning, and unsupervised learning. IBM recommends a foundational understanding of computer science, along with proficiency in linear algebra, statistics, and Python programming.

To earn the certificate, candidates are required to complete six courses offered on Coursera, covering essential topics such as “Exploratory Data Analysis for Machine Learning,” “Supervised Machine Learning: Regression,” “Supervised Machine Learning: Classification,” “Unsupervised Machine Learning,” “Deep Learning and Reinforcement Learning,” and culminating in a “Machine Learning Capstone” project. This comprehensive curriculum equips participants with a well-rounded skill set in machine learning and prepares them for real-world applications in the field.

Microsoft Certified: Azure Data Scientist Associate

The Azure Data Scientist Associate certification, considered the most beginner-friendly among the certifications discussed here, requires passing the “DP-100: Designing and Implementing a Data Science Solution on Azure” exam. Microsoft expects candidates to possess a working knowledge of implementing and running machine learning models on the Azure cloud platform, along with the ability to train predictive models.

The DP-100 exam is structured to evaluate candidates across specific topics:

  • 20%-25% on designing and preparing a machine learning solution.
  • 35%-40% on exploring data and training models.
  • 20%-25% on preparing a deployment model.
  • 10%-15% on deploying and retraining a model.

To assist candidates in their preparation, Microsoft offers six self-paced learning paths, covering critical aspects of the exam’s subject matter, including exploring the Azure Machine Learning workspace, working with data in Azure Machine Learning, automating model selection, training models with scripts, optimizing model training with pipelines, and deploying and consuming models with Azure Machine Learning. These learning paths collectively provide a comprehensive foundation for success in the DP-100 exam and certification.

In conclusion, machine learning certifications form AWS, Google, IBM, and Microsoft provide a crucial foundation in a competitive job market. These certifications validate hands-on experience and offer a comprehensive skill set, covering aspects from data engineering to model deployment.

As AI and ML remain integral to the tech landscape, certified professionals are well-positioned for ongoing demand and innovation.

With this blog post, our aim is to bring the intriguing topic of machine learning closer to you. The key takeaway is to carefully choose a certification that aligns with your goals and to wholeheartedly embrace a continuous learning journey, enabling you to thrive in this dynamic field.