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Machine Learning with TensorFlow on Google Cloud Platform Specialization

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Course Highlights
  • Google Cloud via Coursera
  • 96 hours of effort required
  • 76,863 already enrolled!
  • ★★★★★ (7,952 Ratings)

This is a very interesting specialization that will help learners understand what Machine Learning is with Google Cloud. The specialization contains courses that will help you frame a business use case as a Machine Learning problem. You will get a chance to gain a broad perspective of Machine Learning wherever it can be used. Similarly, the courses in this specialization will help you recognize biases that Machine Learning can amplify. The skills that you will develop from this course are relevant to TensorFlow, Machine Learning, Feature Engineering, Cloud Computing, Application Programming Interfaces (API), inclusive ML, Python Programming, and more. Therefore this and much more can be learned from the courses available in this specialization.

5 Courses in this Specialization:

How Google does Machine Learning (★★★★★ 4.6 | 6,557 ratings | 1,035 reviews)

In this course, you will first learn what machine learning is. Then you will understand the kinds of problems it can solve. After that, the instructor will discuss the five phases of converting a candidate use case that can be driven by machine learning.

Launching into Machine Learning (★★★★★ 4.6 | 4,016ratings | 460 reviews)

This course starts fro, a history of machine learning. Then the instructor will talk about why neural networks in today’s world perform so well in a variety of data science problems. Similarly, you will also learn to set up a supervised learning problem and then find a suitable solution through gradient descent.

Introduction to TensorFlow (★★★★ 4.4 | 2,557 ratings | 314 reviews)

In the third course, the focus will be on using the flexibility and ease of use of TensorFlow 2.x. Along with that, you will also learn to use Keras for building, training, and deploying machine learning models. The instructor will make you learn the TensorFlow 2.x API hierarchy and the main components of TensorFlow through different hands-on exercises.

Feature Engineering (★★★★★ 4.5 | 1,629 ratings | 179 reviews)

Are you interested in finding out how to improve the accuracy of all your Machine Learning models? In this course, you will learn to find which data columns can make the most useful features. The instructor will discuss both good and bad features and then how you can transform them for optimal use in your models.

Art and Science of Machine Learning (★★★★★ 4.6 | 1,344 ratings |118 reviews)

This final course is delivered in the form of 6 modules that covers the essential skills of ML intuition. With that, you will also understand the skills of good judgment and experimentation to tune and optimize ML models.

Feedback:

In this course, we are going to take a look at the feedback which users have given about this specialization or the courses in it.

Positive Feedback:

  • I took the first course from the specialization and it helped me understand well how to do machine learning on scale. I was also able to know the common pitfalls people might fall into while doing ML. The course has provided me with great hands-on training on GCP (JT, ★★★★★).
  • The first course turned out to be very informative as it had a lot to share about machine learning and AI usage in Google products. With that, the course also provided deep information about the GCP platform in a very intuitive way. I recommend this course to everyone interested (VD, ★★★★★).
  • The second course turned out to be an amazing one as it is perfect for beginners like me. This course is a shot in the arm for me. The presentation was excellent, engaging, and very lively and helped me learn a lot about machine learning. Really hoping to see this instructor soon in another course (OD, ★★★★★).
  • This second course was very engaging as it opens up so much inside the story of the ML processes. This course has increased my interest in this field greatly (PT, ★★★★★).
  • I took the third course and would say it was a good experience learning from it. The introduction to TensorFlow 2.0 was very challenging and difficult but totally worth the effort. Nobody said the introduction would be easy so yes you need to put in some effort while learning from this course. Similarly, the instructor Evan Jones was at his best when he explained advanced topics in neural networks (AJ, ★★★★★).
  • I feel like the third course was very valuable because it teaches learners how to create an automated service in the cloud using tons of data. The best part was when the instructor was explaining how to work with distributed systems in the production environment with minimal time (VC, ★★★★★).
  • The fourth course was very great as it covers so much about data pre-processing. The instructor explained well about the tools available in Google Cloud to enable the grueling tasks. I just want to thank the instructor for the amazing lectures and training labs (GS, ★★★★★).
  • I took the fourth course and it turned out to be an interesting one. I believe this is the only course in the specialization that teaches featuring engineering and focusing on production issues (OA, ★★★★★).
  • The last course was a big success as it has so much information to offer. I really appreciate the effort the instructor has put in to break things down for us (MB, ★★★★★).

Negative Feedback:

  • The third course didn’t seem to be about TensorFlow rather it was more about Google Cloud. I couldn’t even learn to install and set up TensorFlow on infrastructure (NJ, ★☆☆☆☆).
  • The lecture videos in the fourth course were good and even better than the last two courses in the specialization but the labs had missing instructions (SA, ★★☆☆☆).
  • The final course was fine and the quality of the lesson material was also great but the quantity of the information was nowhere sufficient to get the hands-on experience (Dimitry B, ★★☆☆☆).

Final Thoughts

So these were the details and reviews about the courses available in this Machine Learning with TensorFlow on Google Cloud Platform Specialization. Now all you have to do is read the description and reviews of all five courses available in this specialization and then decide whether you need to enroll in the whole specialization or one or two courses and never stop learning.

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