Machine Learning with TensorFlow on Google Cloud Platform Review 2024

Coursera has two Machine Learning with TensorFlow course that is developed by Google Cloud. Hence we will review both the Machine Learning with TensorFlow on Google Cloud Platform Specialization course.

More and more people are interested in taking the Machine Learning course. And what’s interesting is even the top technology companies know that Machine Learning is hard to breakthrough. Hence, Google has taken the initiative and designed this course to help people across the globe to study Machine Learning.

In this article, we will cover both Machine Learning with TensorFlow and Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization review. This Machine Learning course is one of the best online course that uses TensorFlow to teach the students.

Scroll below to enroll in a 7-day free trial on Machine Learning with TensorFlow on Google Cloud Platform.

We’ve also covered some FAQ to clear some of the doubts you might have.

FAQ on Machine Learning with TensorFlow on Google Cloud Platform 

Let’s get started to review both the Machine Learning course with TensorFlow on GCP.

Coursera Machine Learning with TensorFlow on Google Cloud Platform Review

This specialization course has five courses and would take approximately one month to complete. Here you learn all the fundamentals of Machin Learning. Even if you are starting from scratch, this is the right course to choose from. 

A lot of people struggle to understand the Machine Learning completely. Hence, this course teaches you the basics of Machine Learning. One may say Machine Learning 101. But it’s more detailed than 101 classes. This course features hands-on labs from Qwiklabs, where you will get hands-on practice in Machine Learning.

Let’s dive into what these five courses in Machine Learning with TensorFlow covers.

1. How Google does Machine Learning?

This is where you finally kick off the ML tutorial by understanding what ML and the kind of problems it solves. According to Google, they think about Machine Learning differently. A general walkthrough on Machine Learning and AI. 

Yes, AI, and Machine Learning. If you’re not aware of this, then you must know that both AI and ML are closely related, but there are differences. Hence, a differentiation in the name. You must learn and understand the difference between AI and ML. You’ll also learn to use their Python notebooks in the Cloud.

2. Launching into Machine Learning

Here you start to discover the history of the Machine Learning on how it all started. You might have overheard someone or read some blog about why Deep Learning and Neural Networks are popular. In the second course, you’ll understand all these essential concepts. It may not seem important in the beginning but must need information. 

You’ll begin to learn ML foundation to familiarize yourself with all the ML terminologies. These terminologies in Machine Learning are essential to accelerate your growth in ML.

3. Intro to TensorFlow

Taking the third course will provide you all the necessary low-level knowledge on TensorFlow. The video lectures here will train you on how to make predictions using Google Cloud Machine Learning Engine. 

Don’t worry if all this sounds complicated. In reality, its not just the high-level technology words but words used in the tech world.

You’ll also find the tutorials to learn TensorFlow API to write models in ML. Once you learn the core concepts of TensorFlow, you will begin performing hands-on labs. Most of the labs here are to build machine learning programs. Then, you’ll learn what an estimator API is and learn how to train an ML model on Google Cloud Platform.

4. Feature Engineering

After you have built the ML model, there should be some way to improve it right. This is where this course shines. Here you’ll learn to improve the accuracy of the ML model that you built in the earlier labs.

The Feature Engineering on Google Cloud Platform will provide insight on useful vs bad features. Learn how you can preprocess and transform your ML model for optimal use. The hands-on lab will give you practical experience on preprocessing ML model on GCP.

To perform preprocessing data, you will leverage the TensorFlow library, tf.Transform. TensorFlow is an interesting subject to get your hands dirty.

5. Art and Science of Machine Learning

Art and Science of Machine Learning is the final course in Machine Learning with TensorFlow on GCP Specialization. To become good at Machine Learning, you need more than skillset – good judgment, intuition, and experimentation. This is the art of Machine Learning by Google.

With the little bit of art, you imply science to perform regularization for sparsity so that we can have simpler, more concise models. Bot the art and science you learn here will be used to build the best performing Machine learning model.

Once you complete the Coursera Machine Learning with TensorFlow on Google Cloud Platform, you can take the advanced course. Let’s review what the Advanced Machine Learning with TensorFlow on Google Cloud Platform offers.

Conclusion of ML with TensorFlow on GCP

One of the best courses in ML if you want to learn Machine Learning. This course can be taken even by people with a non-technical background. However, you should know the Python programming language. 

Coursera Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization Review

The Advanced ML with TensorFlow on GCP is the second part of the previous course. Like the earlier ML course, this course also features five-course and once completed, you qualify for the certificate. It is important to note that this is the continuation of the previous class and picks up where it left off.

Here you’ll get more hands-on practice to do Machine Learning models and learn more. Taking this advanced course will make you more experienced in Machine Learning. This will be a turning point in your career, for sure. 

The Advanced Machine Learning tutorial will concentrate more on the optimizing, deploying, scaling your ML models. Let’s review by diving into each of the five courses from Advanced Machine Learning with TensorFlow on Google Cloud Platform.

1. End-to-End Machine Learning with TensorFlow on GCP

If there are some gaps in the taking the advanced ML course, do not worry. Here the Machine Learning video classes will recap the previous course of TensorFlow on GCP. This course also features a workshop in Machine Learning to recap what you’ve learned in the earlier class. 

The workshop features end-to-end Machine Learning with TensorFlow on GCP. This is the most effective way to know what you’ve learned so far. Here, you’ll also explore some advanced concepts that require some familiarity with the SQL. The introduction of Datalab and the BigQuery will help in preprocessing the data. 

2. Production Machine Learning Systems

The second course in the Advanced Machine Learning with TensorFlow will concentrate on the best practices of ML systems. That, too, in the production environment. You’ll find some tutorials to make some high-level decisions on training a model and improve the performance. 

Also, it is important to work with data. So bringing data to the Cloud is essential, and you must know the importance of it. You’ll learn all this in the second course of the Advanced ML course. You’ll learn the different types of ML models and know when you should adopt the hybrid ML models.

3. Image Understanding with TensorFlow on GCP

The key to improve your career in ML is to know the different strategies of building a Machine Learning model. That is where this course shines on where you learn to implement an image classifier using CNN (convolutional neural networks). You’ll get hands-on practice to build and optimize your very own image classification models.

It also focuses on securing your data on the Cloud, which is crucial in the Machine Learning class. Make sure you give some extra attention to this class. While building an ML model, there are some real-world issues where you don’t have enough data. This scenario comes in a lot of situations, and you’ll learn everything about it.

4. Sequence Models for Time Series and Natural Language Processing

This is the fourth course and an introduction to sequence models and their applications. Also, you’ll get an overview of the sequence model architectures. Here you learn about the text classification and sequence models by working on a variety of datasets. 

Some of the key objectives of this course is predicting the future values of time series, address time series, text problems with RNN, classify free form test, and train and resue word embeddings in-text problems. You will do all this by using interactive Machine Learning labs from Qwiklabs.

5. Recommendation Systems with TensorFlow on GCP

In the final chapter of the five-course module, you will use all the recommended tools available on GCP to build a Recommendation Systems. 

Once you complete the final course and all the projects in the course, you will earn a certificate from Coursera on Machine Learning. Showcase all the ML skills you learned in your professional network and with your new potential employer. For more practice, try some of the TensorFlow sample projects.

Conclusion of Advanced ML with TensorFlow on GCP

This course is recommended to be taken only after the first course. This course is the advanced course in ML, and hence it is a prerequisite to complete the earlier course. The best-advanced ML course to upgrade your career. After you complete the course take the online guided-projects on Machine Learning from Coursera to get more exposure.

Summary

The Machine Learning with TensorFlow on Google Cloud Platform is an ideal choice to take Machine Learning training. This course featuring Cloud is the most in-demand course, and many companies are looking for candidates with ML exposure. 

If you’re still unsure, please ask your questions on the below comments, and we’ll get back to you. We’ve also listed some of the best Machine Learning training that you should know.