Machine Learning Certification

Top 5 Machine Learning Certification Programs

The demand for professionals with knowledge in AI has never been greater. As companies continue investing resources into artificial intelligence, they increasingly require their employees to have an AI skillset. Below we’ll outline ways to get started in this exciting field and the type of machine learning certification programs that are available today.

Our Top 5 Picks

Udacity Logo
AWS Machine Learning Engineer Nanodegree
Best AWS machine learning certification
Machine Learning Scientist Career Track
Best Python machine learning program
Machine Learning Introduction with Python
Best starter machine learning program
Harvard University
Professional Certificate in TinyML
Best TinyML professional certificate
Udacity Logo
Azure Machine Learning Engineer Nanodegree
Best Azure machine learning certification

1. AWS Machine Learning Engineer Nanodegree (Udacity)

Udacity Logo

Get the skills you need to build powerful machine learning applications from the 5-month AWS Machine Learning Engineer Nanodegree program. Learn in collaboration with AWS and take the first steps towards a career in machine learning.

Throughout the program, you will gain the skills you need to build, deploy, and manage machine learning models on Amazon SageMaker. Learn from the experts and get the training you need to be a successful ML engineer.


  1. Introduction to Machine Learning – Learn about machine learning concepts and how to use them for real-world scenarios in this beginner-friendly course. You will learn the basics of performing exploratory data analysis, creating machine learning workflows, and performing basic feature engineering. Plus, you’ll learn how to build sophisticated ML models and make predictions with XGBoost and AutoGluon.
  2. Developing Your First ML Workflow – Gain the knowledge and skills you need to create effective machine learning workflows on AWS. In this course, you’ll learn the foundations of machine learning, from the basics of data preparation and preprocessing to model training and deployment. Next, this course also covers how to use SageMaker to develop and deploy models, as well as how to evaluate the performance of your models. Finally, you will learn how to build a successful machine learning pipeline with services like Model Monitor and Feature Store.
  3. Deep Learning Topics within Computer Vision and NLP – In this course, you will learn about deep learning, artificial neural networks, and computer vision. You will also be able to use these techniques to train models and deploy them on Amazon SageMaker. Throughout the course, you will get a deeper understanding of how computer vision and NLP are used in real-world applications. You will also get hands-on experience with SageMaker Studio IDE for ML development and deployment.
  4. Operationalizing Machine Learning Projects on SageMaker – In this course, you will get hands-on experience with deploying machine learning projects on SageMaker. Plus, you will learn how to optimize your models, manage and troubleshoot your projects, and deploy them into production.
  5. Capstone Project: Inventory Monitoring at Distribution Centers – This project is for students who are interested in learning about using AWS Sagemaker for inventory management. The goal of this project is to develop a model that is capable of counting the number of objects in a bin. Training a machine learning model to identify inventory can improve the accuracy of data collection and reduce the amount of time spent on inventory monitoring. Upon completion, students can include this in their personal portfolio to showcase to future employers.

Skills Acquired

  • Amazon SageMaker
  • XGBoost
  • AWS
  • AutoGluon
  • SageMaker Studio
  • Feature Engineering
  • Hyperparameter Tuning
  • Lambda Functions
  • Step Functions
  • Model Monitor
  • Feature Store
  • ML Pipelines
  • Convolutional Neural Networks
  • Computer Vision
  • NLP
  • ML Workflows

For more information, read our review of the Udacity Machine Learning Nanodegree.

  • PREREQUISITES: This program requires basic knowledge of machine learning algorithms and Python.

2. Machine Learning Scientist with Python Career Track (DataCamp)


Get into the world of machine learning with the Machine Learning Scientist with Python Career Track. This program will train you to become a professional ML scientist using libraries like Keras and TensorFlow.

With this career track, you will learn about machine learning, natural language processing, and image processing. Finally, you’ll learn to build and optimize ML models and assess their performance.

Simplified Courses

  1. Supervised and Unsupervised Learning with scikit-learn – Learn to make predictions and cluster datasets with k-means and scikit-learn. Next, it will teach you linear classifiers and tree-based models for regression.
  2. Introduction to Natural Language Processing in Python – You will learn how to extract insights from text data by using natural language processing techniques and feature engineering.
  3. Introduction to TensorFlow in Python – This course will teach you the basics of neural networks, deep learning, and TensorFlow. You will learn how to build models and optimize them for accuracy and performance.
  4. Advanced Deep Learning with Keras – Get a deep understanding of how to build and train deep learning models with Keras. This course covers everything from convolutional neural networks to image processing.
  5. Machine Learning with PySpark – Learn how to make predictions using regression, Big Data, and PySpark.

Skills Acquired

  • Machine Learning
  • Supervised Learning
  • scikit-learn
  • Python
  • Unsupervised Learning
  • scipy
  • Linear Classifiers
  • Logistic Regression
  • Tree-Based Models
  • Extreme Gradient Boosting
  • XGBoost
  • Cluster Analysis
  • k-means
  • Dimensionality Reduction
  • Data Preprocessing
  • Feature Engineering
  • Time Series Data
  • Model Validation
  • Natural Langauge Processing
  • TensorFlow
  • Deep Learning
  • Keras
  • Image Processing
  • Hyperparameter Tuning
  • PySpark
  • INFORMATION: This program contains 23 courses in a duration of 93 hours.

3. Machine Learning Introduction with Python Skill Path (DataQuest)


Get ready to learn how to use Python to clean and correct errors in data, and make predictions using machine learning. The Machine Learning Introduction with Python Skill Path is perfect for anyone who is just starting out and wants to get their hands dirty.

Throughout the program, you will learn to master various components and techniques of machine learning. Some of the topics this program covers include deep neural networks, logistic regression, and linear algebra.

Simplified Courses

  1. Python Introduction – At the start of the program, you will gain a solid understanding of Python. Specifically, you’ll learn how it relates to analysis, visualization, and preprocessing. During this introductory course, you will learn about core libraries like Numpy, pandas, Matplotlib, and Seaborn.
  2. Statistics – Once you are fully familiar with Python, this program transitions into statistics. For instance, this course covers topics like sampling, probability, and variability, which are key concepts of statistics.
  3. Machine Learning with Python – Learn the basics of machine learning in Python with this course. You will start with the basics of linear algebra and calculus. Next, you will work your way up to more advanced machine learning techniques like decision trees and deep learning.
  4. Machine Learning Project – During this project, you will complete this skill path with machine learning that includes topics covered in the program. First, you will clean and prepare data using Python. Finally, you will learn how to use machine learning to predict a real-world loan risk assessment.

Skills Acquired

  • Machine Learning
  • Python
  • Jupyter Notebook
  • Matplotlib
  • Seaborn
  • pandas
  • NumPy
  • Data Cleaning
  • Statistics
  • Probability
  • Variance
  • Bayes’ Theorem
  • K-nearest Neighbors
  • Calculus
  • Linear Regression
  • Decision Trees
  • Deep Neural Networks
  • Activation Functions
  • Multiple Hidden Layers
  • Image Classification
  • INFORMATION: This self-paced program is beginner-friendly with 21 courses and 17 projects.

4. Professional Certificate in Tiny Machine Learning TinyML (Harvard University)

Harvard University

Get an online certificate in tiny machine learning (TinyML). Throughout the program, you will get the skills you need to create effective TinyML machine learning models that you can train and deploy.

The Professional Certificate in Tiny Machine Learning TinyML is a 3-course program from Harvard University. Overall, this program trains you in the fundamentals, applications, and deployment of TinyML models.


  1. Fundamentals of TinyML – This course will help you understand embedded TinyML, which you can use to power smartphones.
  2. Applications of TinyML – Take the first step in learning how to train TinyML applications. Watch examples of TinyML applications in action, and gain the skills and knowledge you need to start training your models for tiny applications.
  3. Deploying TinyML – Get started with TensorFlow Lite today and learn how to write code and deploy your model. With this course, you’ll get hands-on knowledge for TinyML model deployment which can be used as a stepping stone to creating a TensorFlow Lite application.

Skills Acquired

  • Tiny Machine Learning
  • TensorFlow Lite
  • Model Deployment
  • Deep Learning
  • Embedded Devices
  • Optimization
  • Resource-Constrained Devices
  • Microcontroller
  • PREREQUISITES: This self-paced program requires basic programming in C/C++ or Python.

5. Microsoft Azure Machine Learning Engineer (Udacity)

Udacity Logo

Get the skills you need with the online Machine Learning Engineer for Microsoft Azure Nanodegree from Udacity. This 3-month program will train you in machine learning to validate models and evaluate results.

Throughout the program, you will gain experience working with popular open source machine learning tools and frameworks. Finally, you will wrap up this program with a Capstone Project involving Azure’s Automated ML and HyperDrive.


  1. Using Azure Machine Learning – Learn how to use Azure Machine Learning to tackle business problems and identify use cases.
  2. Machine Learning Operations – Get up to speed with the key concepts and techniques needed to ship machine learning models into production. This course covers Application Insights, logs, and pipelines.
  3. Capstone Project – Use the knowledge you have acquired from this Nanodegree program to solve an ML problem. First, you will use Azure’s Automated ML and HyperDrive to train a model. Next, you will deploy the model as a web service and endpoint for validation.

Skills Acquired

  • Microsoft Azure
  • Machine Learning
  • Azure ML SDK
  • ML Pipelines
  • AutoML
  • Application Insights
  • Logging
  • HyperDrive
  • Deep Learning
  • CNN + ANN
  • Keras
  • TensorFlow
  • PyTorch
  • Model Web Service Deployment
  • PREREQUISITES: This program requires basic knowledge of Python, machine learning, and statistics.

The 5 Best Machine Learning Certification

With the rapidly growing importance of data-driven decision-making, businesses require employees who can leverage machine learning and artificial intelligence to make critical business decisions.

That’s where machine learning certification comes in.

Have you tried any of these machine learning certification courses? If you have we’d love to hear from you!

Please include any feedback in the comment form below. What did you like or dislike about any of these programs?

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