Google releases machine learning crash course, other educational AI resources In September, 2023



Learn with Google AI: Making ML education available to everyone

( A self-study guide for aspiring machine learning specialists )


Machine learning and AI are a portion of the greatest points in the tech world right now, and Google is hoping to make those fields more available to more individuals with its new Learn with Google AI site.

Google has been seeking after AI education for some time, both with cutting edge ventures like TensorFlow and more perky tasks and a machine vision explore intended to feature AI projects in more reasonable ways. Google imagines the Learn with Google AI site filling in as an archive for machine learning and AI, and it’s intended to be a center for anybody hoping to “find out about core ML ideas, create and sharpen your ML skills, and apply ML to real-world issues.

The site will clearly take into account all levels of AI lovers, from analysts searching for cutting edge instructional exercises to tenderfoots. The site likewise includes a free course called Machine Learning Crash Course (MLCC). The course is based off an inner Google course that was initially intended to enable provide for google employees a reasonable prologue to AI and machine learning basics, with ~20,000 workers as of now enlisted.

Presently, Google is making MLCC accessible to everybody through the Learn with Google AI site, giving activities, interactive visualizations, and instructional videos to enable educate to machine learning ideas. The course is recorded as approximately 15 hours, with interactive lessons, addresses from Google analysts, and more than 40 practices included.

It’s intended for newcomers with no machine learning background at all, in spite of the fact that Google recommends that course takers have a dominance of introduction level math and some capability in programming rudiments and Python. The MLCC is likewise the first of what Google plans to be numerous courses and assets accessible through the new center point, with additional to come soon.


Google has made AI available by giving lessons, instructional exercises and hands-on practices for individuals at all experience levels. You can Filter based on

  • Individuals (Researcher, Data Scientist, Software Engineer, Student, Business Decision Maker, Curious Cat ) 


  • Type of Content (Interactive, Video, Documentation, Tutorials & Code Labs, Courses, Sample Code)


  • Stage of ML Development ( Developing the Idea, Data Collection, Data Preparation, Model Construction, Model Evaluation, Model Deployment)



Machine Learning Crash Course from Google includes :


  • Series of Lessons with Video Lecturers from Google Researchers


  • Real-World Case studies


  • Hands-on Practice Exercises


  • Interactive Visualizations of Algorithms in action  etc



Pre-Requisites : Click Here

Exercises : Click Here

Tools : Click Here

Explore Google Research : Click Here


You can start the course Right Here


My View Point on Machine Learning Crash Course


After Google discharged their AI educative assets on the web, I additionally looked at them.

I experienced the ML Crash Course and endeavored to get a thought what was happening in MLCC course.

Few highlights I saw between MLCC versus Professor Ng’s Machine Learning course:

Coursera’s Machine Learning looks heavier than Google’s MLCC.

MLCC is efficient with videos, perusing materials and programming works out. Advantageously, you won’t have to introduce Python libraries or TensorFlow all alone machine to attempt the activities as they are facilitated online in Jupyter Notebook. This is alleviating for tenderfoots. Then again, Professor Ng’s course needs MATLAB/Octave to be introduced without anyone else machine. In any case, this course is truly efficient, as well.

As a programming language for Machine Learning, MATLAB isn’t anyplace close Python in fame and group bolster. Python is somewhat wherever in this field of innovation. Indeed, in Deep Learning Specialization by Professor Ng, programming part is done in Python.

Professor Ng’s course is more similar to a formal course. You have tests on every week’s topics alongside programming assignments. You need to complete those activities inside due dates and submit them on the web. At that point there is this reviewing system which can help you to keep inspired and centered. This isn’t the situation for ML brief training.

If you want to learn every day every week and want motivation, course of Coursera holds good.

Professor Ng shows all the related subjects actually starting with no outside help. He instructs the ML ideas in great detail.The assignments are composed such that you won’t need to compose much code. Just center couple of lines of code are sufficient to complete the assignments. Furthermore, they are simple.

Conversely, ML brief training expects some sort of nature with Python. It utilizes a more elevated amount API of TensorFlow called tf.estimator. So you will do things utilizing a more elevated amount API and a lot of them will most likely occur in the engine as the API will deal with bunches of things for you.

The above point drives us to another imperative point: in the event that you are just a beginner, it is more probable that you will feel very awkward with TensorFlow alongside all the broad uses of Python libraries. For this situation, Google’s compressed lesson won’t suit you.

Mathematics knowledge isn’t exactly critical in any of these courses. Since Google utilizes TensorFlow to do the activities, arithmetic will for the most part go in the engine. Actually they have made the Calculus of slope drop discretionary to look at in one of their perusing materials.

Correspondingly, Professor Ng accept that the understudies don’t have a clue about any Calculus or Linear Algebra. He maintains a strategic distance from complex scientific verification and inductions with the goal that the materials are available to everybody totally.

At long last, Professor Ng is, with no uncertainty, an extraordinary instructor. He discusses loads of different parts of Machine Learning separated from the center points in his course. These merit tuning in to.


Final Words, on the off chance that somebody asks me which one he should take, I will propose the course by Professor Ng. This course has started the trip for some, ML engineers. It will clearly help you.

However, it is likewise evident that on the off chance that you don’t take the Coursera’s course and choose to look at the other one, recollect that there is unquestionably no damage in it. The substance are rich. You will in fact be profited.


Note: If robots are coming to take your job, this course will prepare you for your next job:)


Source: Google

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