Best AI & Machine Learning Online Course with Certificate

Table of Contents hide 1 5 Popular Machine Learning Certifications to Opt-in 2023 1.1 TensorFlow Developer Certificate 1.2 Machine Learning Crash Course...

Written by Niel Patel · 7 min read >
ml certifications

Today we are talking about the top machine learning certifications that you guys should be looking at when you are learning machine learning. And also which type of certifications would look best on your resume when you’re applying for those machine learning roles jobs.

When we’re looking at machine learning certifications, there are two different types that we need to know. There’s a very general machine learning certification, and then there are specialized machine learning certifications. 

  1. General machine learning certification
  2. Specialized machine learning certifications

Both of them have their pros and cons, and team Make An App Like are going to cover both. In this article, we’re going to be looking at both specialized machine-learning certifications and general machine-learning certifications. 

When you’re deciding which certification is suitable for your courier, there are a lot of different factors to consider. You must consider what is the reason behind getting the best AI ML certification. If you’re planning on applying for roles and you know that the role that you’re applying for deals with, for example, Microsoft Azure, you would want to get a Microsoft Azure-related certification for ML. 

You wouldn’t want to go and get a certification made by AWS or Google instead. So these are also very important things to consider. And also you might want to consider what is the depth of knowledge of ML that you actually want to showcase to your employers. 

5 Popular Machine Learning Certifications to Opt-in 2023

TensorFlow Developer Certificate

The very first certification that we’re going to be looking at is the TensorFlow Developer certification. With this certification, you’re able to demonstrate that you know how to use TensorFlow, which is a very popular machine learning free, and it’s widely used in the industry.

So if you’re starting out, this is an excellent certification to have. Let’s also go and take a look at how the test is and how much this certification cost, as well as more details. So in order to get this certification, you need to write a 1 hour long. 

Exam and TensorFlow certification cost $100.

The great thing about this certification personally, I feel, is that the resources are very easily available online, and there are a bunch of courses that are already on this website that you can take, or there are also a lot of tutorials that you can follow along in order to prepare for this certification. 
When you guys complete this certification, you would have successfully learned some foundational principles of ML and deep learning.

You should also know how to build ML models in TensorFlow, as well as build image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks. 
You should also know how to use real-world images in different shapes and sizes in order to visualize the journey of an image through different convolutional networks, as well as explore strategies to prevent overfitting, including augmentation and dropouts. 

So the interesting thing with this TensorFlow certification, I feel, is that it is very much specific to image recognition, so computer vision and a little bit of natural language processing.

Although TensorFlow is a more general-based machine learning library that you can use for different use cases, this certification highly focuses on computer vision. Once you register for the TensorFlow certification exam, you have about six months to actually take the exam. And when you’re taking the exam, you have 5 hours to complete the entire exam. Although you have 5 hours, it usually takes about one to 2 hours to actually complete the exam. 

And once you have done that, it takes only 24 hours for it to get graded, and then you will be given your certification. Once that is done, you can easily share this onto GitHub or LinkedIn, as well as putting it onto your resume so that this is more easily accessible than going and taking a test in person. 
Another really great thing is that with this certification, once you’re done with it and you have achieved the certification, you will be automatically added to a pool of developers who have the certification so that if employers are reaching out to any developers within that pool, you will be included in that as well. 

The second certification which I’m going to be covering is Google Cloud’s Professional Machine learning engineering certification. This certification is a lot more in-depth than the TensorFlow certification, and it covers very different skills as well. 

Machine Learning Crash Course

Machine Learning Crash Course
with TensorFlow APIs

In order to get that, you should know how to frame ML problems. You need to know how to architect ML solutions, as well as design data preparation and processing systems as well, and also automating and orchestrate ML pipelines. 

This is a more end-to-end certification than the previous TensorFlow certification. With the TensorFlow certification, you are basically testing your ability to use a machine-learning library. But for this one, it’s more end-to-end. 

You should know how to frame machine learning problems, develop ML solutions, automate and orchestrate ML pipelines, and also optimizing and maintain machine learning solutions as well. In order to get the certification, it takes about 2 hours to complete the exam, and the registration fee for this certification is $200. 

The entire certification is either in multiple choice or multiple select. And you can also renew or maintain your certification because these Google Cloud certifications, its only valid for two years. 
So if you look at Google Cloud’s products, and if you look under AI and ML, when you’re applying for this certification, you should actually know how to use quite a lot of these different Google Cloud products already. 
For example, vertex AI, as well as natural language AI products. As well as using Google Cloud in order to deploy ML models. So these are important skills that you should know in order to apply for this certification. 

3. Microsoft Certified: Azure AI Engineer Associate

The third certification we will be looking at is by Microsoft and it’s called the Microsoft Certified: Azure AI Engineer Associate. And although this is called the Azure Data Scientist Associate, this certification is very much to ML. 

CERTIFICATION EXAM: Designing and Implementing a Microsoft Azure AI Solution

Cost: $165 USD*

Two ways to prepare for the Microsoft Azure ML exam.

  1. Self Paced
  2. Instructor-led courses

Skills measured

Let’s look at some of the skills which are actually measured within this certification. So managing Azure resources for ML running experiments and training models deploying and operationalizing machine learning solutions implementing responsible ML. 

  • This list contains the skills measured on the exam required for this certification.
  • Plan and manage an Azure AI solution
  • Image and video processing solutions
  • Implement natural language processing solutions
  • Implement knowledge-mining solutions
  • Conversational AI solutions

So regarding all of these certifications, like the Google certification previously, as well as this one, all of these certifications are catered towards this tech company’s suite of products for ML. So the Google certification is catered around Google Cloud. 

And with Microsoft, it’s catered around Microsoft Azure. So depending on the type of machine learning role that you’re applying for and the type of company that you’re applying for, look at what type of resources they use. 

If they are making use of Google Cloud, then go for the Google Cloud certification. If they’re making use of Microsoft Azure, go with this because that will best suit your resume as well as it will really help you in your career as well. 

So there’s a lot of resources already available for this program and this exam cost 165 USD In the United States.

4. AWS Certified Machine Learning

And last but not least, we will be looking at the AWS Certified Machine Learning Certification.  This certification is a specialty that is offered by AWS. Let’s take a look at who should take this exam. Someone with at least two years of hands-on experience developing, architecting and running ML or deep learning loads in the AWS Cloud. 

Exam overview

  • Level: Specialty
  • Length: 180 minutes to complete the exam
  • Cost: 300 USD 
  • Visit Exam pricing for additional cost information.
  • Format: 65 questions; either multiple choice or multiple responses.
  • Delivery method: Pearson VUE testing centre or online proctored exam.

Download the exam guide

Download the sample questions

Ability to express the intuition behind basic machine learning algorithms, so understanding the math behind it, et cetera. Also, experience performing basic hyperparameter optimization experience with ML and deep learning frameworks. 

Ability to follow model training, deployment and operational best practices. So there are some basics and obviously having some experience, like for example, two years of experience which they are recommending that you should in order to have an understanding of how to use AWS. 

Cloud have an understanding of how to deploy models on AWS, et cetera. So it definitely is a more advanced certification and the length of time required to complete the exam is 3 hours. So it’s a pretty lengthy exam and it cost about $300 for this certification.

Few ways to prepare for AWS machine learning exam

4 different domains of Machine learning

  • The first domain is data engineering.
  • The second is exploratory data analysis.
  • The third is modelling.
  • And fourth is machine learning implementation and operations. 

So, although data engineering is, of course, an entirely different role from a machine learning engineer, there’s a lot of overlap between these two types of roles. And as an ML engineer, it’s also important that you know a lot of. 

Data engineering best practices, because you’ll definitely be using that in your day-to-day. So, for example, using AWS Kinesis or AWS EMR, are different AWS tools that are specific to data engineering. 

And it’s important that if you’re applying for this certification, you know how to use those tools. So in the next domain of Exploratory data analysis, they are looking at Sanitizing and preparing data for modelling.

Obviously in the date, Sanitizing is similar to cleaning data for modelling, knowing how to perform feature engineering, and analyzing and visualizing data for machine learning in different histograms time series, scatter plots and various other diagrams. 

The third domain is modelling. So framing business problems as machine learning problems, and knowing and selecting the appropriate models for a given machine learning problem. So you should know a lot of these very popular ML models. 

For example, boost logistic regression, k means linear regression, decision trees, random forest, and many, many more. So these are some very basic ML algorithms, which you should already know and you should know when to use them and which data is best suited for which algorithm. 

5. Machine Learning CS229 Stanford School of Engineering

  1. Format – Online, instructor-led
  2. Time to Complete – 10 weeks, 15-25 hrs/week
  3. Tuition – $4,200.00 – $5,600.00
  4. Course Material – Course Website
  5. Academic credits – 3 – 4 units
  6. Credentials – Stanford University Transcript

You should also know how to train ML models and also choose the different computing choices.

  1. So GPU versus CPU, distributed versus nondistributed, as well as platforms.
  2. So spark versus non-spark.

If you don’t know a lot of these things right off the bat, but when you are preparing for this certification, it’s important that you consider all of these things, because these will be tested in the exam. So you should know what these are by the time you are actually taking the exam.

10 More Machine Learning Courses for 2023

  1. Machine Learning cource from Stanford University
  2. Machine Learning Foundations: A Case Study Approach (University of Washington)
  3. Machine Learning for All (University of London)
  4. Machine Learning with Python (IBM)
  5. Machine Learning (Georgia Tech)
  6. Machine Learning Crash Course with TensorFlow APIs (Google)
  7. Machine Learning A-Z: Hands-On Python & R In Data Science (Udemy)
  8. Introduction to Machine Learning in Production (DeepLearning.AI)
  9. Python for Data Science and Machine Learning Bootcamp (Udemy)
  10. Machine Learning for Musicians and Artists (Goldsmith)
Which is the best online machine learning course?

Google Cloud and TensureFlow is the online machine learning course available in 2023.


There are four different types of Machine Learning Certifications we have. The three which are centred around Google Cloud, AWS, and Microsoft Azure are catering towards their cloud services. 
So it’s important that you make the right decision when you’re picking between these three because obviously, you want to make the best use of this certification. You can go ahead and get multiple of them, but it definitely makes sense to get the certification that is best suited to you and also the certification in the resource that you plan on using in the future. 
So if you plan on working with Google Cloud, go ahead and get the Google Cloud certification.

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