We’ve talked about how Artificial Intelligence (AI) and its subfields are taking over the Information Technology industry, and how they drive the salaries higher than any other fields, countless times.

So, let’s cut that part. If you are a frequent visitor to our website, you’ve probably already read our other articles about FREE courses from some of the top universities or companies. If you haven’t here are some of them:


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In this article we are going to talk about some of the best FREE AI related courses by Google. At this point, you know who Google is, so there is no need to explain to you that, but have you heard about Google AI? It is basically a hub where you can find tons of AI, materials, datasets, papers, projects, etc., or in fancy Google words: “At Google AI, we’re conducting research that advances the state-of-the-art in the field, applying AI to products and to new domains, and developing tools to ensure that everyone can access AI”.

You can find tons of useful stuff here, and today we are going to help you learn something more, by presenting the best FREE courses by Google. Also, read here how to start with Machine Learning with Swift for Apple Devices.


The best FREE AI courses by Google:



  1. Clustering in Machine Learning

In this course you are going to learn about:

  • Define clustering for ML applications.
  • Prepare data for clustering.
  • Define similarity for your dataset.
  • Compare manual and supervised similarity measures.
  • Use the k-means algorithm to cluster data.
  • Evaluate the quality of your clustering result.

The estimated time for this course is 4 hours.


  1. Introduction to Machine Learning Problem Framing

In this course you are going to learn about:

  • Define common ML terms
  • Describe examples of products that use ML and general methods of ML problem-solving used in each
  • Identify whether to solve a problem with ML
  • Compare and contrast ML to other programming methods
  • Apply hypothesis testing and the scientific method to ML problems
  • Have conversations about ML problem-solving methods

The estimated time for this course is 1 hour.


  1. Recommendation Systems

In this course you are going to learn about:

  • Describe the purpose of recommendation systems.
  • Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.
  • Use embeddings to represent items and queries.
  • Develop a deeper technical understanding of common techniques used in candidate generation.
  • Use TensorFlow to develop two models used for recommendation: matrix factorization and softmax.

The estimated time for this course is 4 hours.


  1. Testing and Debugging in Machine Learning

In this course you are going to learn about:

  • Validate raw feature data and engineered feature data.
  • Debug an ML model to make the model work.
  • Implement tests that simplify debugging.
  • Optimize a working ML model.
  • Monitor model metrics during development, launch, and production.

The estimated time for this course is 4 hours.


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  1. Data Preparation and Feature Engineering in ML

In this course you are going to learn about:

  • Recognize the relative impact of data quality and size to algorithms.
  • Set informed and realistic expectations for the time to transform the data.
  • Explain a typical process for data collection and transformation within the overall ML workflow.
  • Collect raw data and construct a data set.
  • Sample and split your data set with considerations for imbalanced data.
  • Transform numerical and categorical data.

The estimated time for this course is 3 hours.


  1. Intro to Fairness in Machine Learning

In this course you are going to learn about:

  • Become aware of common human biases that can inadvertently be reproduced by ML algorithms.
  • Proactively explore data to identify sources of bias before training a model
  • Evaluate model predictions for bias

The estimated time for this course is 1 hour.


  1. Machine Learning crash course

In this course you are going to learn about:

The estimated time for this course is 12 hours.

This is probably the most important course if you are new to Artificial Intelligence.


  1. Deep Learning Nanodegree Foundation (Udacity: Intro to TensorFlow for Deep Learning)

In this course you are going to learn about:

  • Building your image classifiers and other deep learning models.
  • Using TensorFlow models in the real world on mobile devices, in the cloud, and in browsers.
  • Advanced techniques and algorithms to work with large datasets.

The estimated time for this course is 2 mounts.


  1. Data Engineering on Google Cloud Platform (Coursera: Data Engineering, Big Data, and Machine Learning on GCP Specialization)

In this course you are going to learn about:

  • Processing big data at scale for analytics and machine learning
  • Fundamentals of building new machine learning models
  • Creating streaming data pipelines and dashboards

The estimated time for this course is 54 hours since it is a specialization and consists of 5 sub-courses.




These are the top FREE Artificial Intelligence, Data Science, Machine Learning, Deep Learning specializations and courses by Google according to us. Don’t forget to buy some of the books that we recommend you, to learn even more and become an expert in this field.

As we already said in many of our previous articles related to FREE AI courses, there is TONS and TONS of free educational materials from some of the best universities and companies in the world, so do not miss this chance to learn, since everybody needs skilled employees in an area where salaries are around $100,000 per year.

Companies and organizations are pouring billions of dollars into these FREE materials, and it would be a waste if you miss this.

So, choose some of the programs that suit you the best, learn, practice, create your own projects, and then find the jobs of your dreams, in these top companies, or in some of your local companies, or even start your own startups, projects, whatever, the big companies are ready to buy and invest in everything.

To help you chose the best program for you, here are our suggestions:

If you are interested in becoming a Creator at Laconic Machine Learning, check out our program here.

Like with every post we do, we encourage you to continue learning, trying, and creating.

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