Very shallow course content.

The lab is outdated

In this course, I learned How to use the Google cloud platform(GCP) and it's tools like BigQuery, Cloud Storage, Vision, Dataproc, Pub/Sub, Dataflow, compute engine, etc.

In GCP, we can generate the instance of Virtual Machine(VM). It's a serverless platform (Google has it's own data centers). We can develop a complete software through GCP.

IN GCP, we can build custom models. It is very handy to operate for BigData. The Data in GB, TB, or PB can be processed in seconds or minutes on GCP.

Also, I deployed the ML model for Classifying Images with Pre-built ML Models using Cloud Vision API and AutoML.

In this model, we classified the Images of clouds in three categories., viz cirrus, cumulonimbus, and cumulus.

The cool features of AutoML and Vision API-

-We don't have to write code for building the machine learning(ML) model.

-AutoML decides the dataset splits for training and testing.

-If you are working with a dataset that isn't already labeled, AutoML Vision provides an in-house human labeling service.

- We have to just evaluate the model by adjusting the Confidence threshold and the confusion matrix.

Sometimes the training time will be more because of large datasets, node training time as well as infrastructure set up and tear down.

Though it is cost-efficient because you have to pay for the memory you use, The time processing takes place(for Training the nodes in ML), etc.

The bottom line is GCP offers IaaS (Infrastructure as a Service) in the form of Google Compute Engine, and it offers Paas (Platform as a Service) in the form of Google App Engine. As for FaaS (Function as a Service), GCP offers it in the form of Google Cloud Functions.

I am fascinated to learn how Google Cloud successfully builds applications that use our big data and machine learning products. This course helped me to understand real-world data and ML challenges and gave practical hands-on expertise in solving those challenges using Google Cloud Qwiklabs.What were the challenges faced?1. Migrating existing big data workloads to an environment where we can effectively analyze all of your data, interactively analyzing large datasets using BigQuery2. Building scalable pipelines that can handle streaming data, so that businesses can make data-driven decisions more quickly using Cloud Pub/Sub and Cloud DataFlow3. Building machine learning models(recommendation, prediction and classify images) so that we are able to make predictive forward-looking actions using our data using Cloud SQL, Spark, VisionAPI and Cloud AutoML

Valuable overview of central big data topics and how they are supported by Google Cloud Platform. Labs are clear and well thought out, but I did have some struggles with a minor misalignment between what I saw upon "open console" - (a sign-in window), and what the lab expected me to see (a "choose account" window). When I viewed the qwiklabs overview video again, it then became clear to me that I needed to simply go ahead and sign in with the credentials provided by the lab. It might be appropriate to update the lab instructions accordingly.

A course which is must to be done by a beginner who like to come to the cloud world as a beginner. Very interesting sessions mixed up with hands-on labs will give you a lots of confidence to make experiment on GCP without any other tutorials. Almost all the products and technologies are well explained not only the concepts but the actual real world scenario where we can suggest and implement the GCP tools. This course extremely boosted my confidence to go for my next goal which is to become a Google certified Professional Data Engineer.

I'm a machine learning Student. Every time I tried to apply my theoretical knowledge to a real problem, I was unable to do so by the limits of my local machine. I tried to learn how to use GCP, but being much closer to a mathematician than a computer expert, I found it very difficult to do it alone.

This course made the general guidelines for the GCP environment very clear, showing me all the tools I could need for the different cases of use. I can not wait to continuing with the dedicated Tensorflow module.

I took this Google Cloud Platform Big Data and Machine Learning Fundamentals without any knowledge on GCP. I strongly recommend this course for professional who willing to step in to cloud analytics and data engineering . This course really helped me to push more to learn and dive in to MLE and other cloud platform tool. Coursera designed the course in such a way it will be definitely helpful for professionals who want to achieve in Cloud Platform. You can learn at anytime and anywhere.

There were some frustrating tech glitches when I started, e.g., the lab just sits there after you agree to the policies dialog when you start each lab, that I eventually learned is a known bug that is fixed by refreshing the browser. But other than those sorts of things the class was very good. Taking the class in person helped me get past the lab setup, logging in in an incognito window, copying the lab-generated credentials, etc., that I may not have been able to get past on my own.

I wish the course did a better job of identifying each GCP service with it's most important features.

Having so many products (many of which offer similar, or similar sounding jargon/names) it was quite tricky to encode it to my memory. Even with repetition learning was a bit tedious.

Other than that however, I loved the easy to listen tone of speaking and "good vibe" from Lax. He made the course overall a very positive experience. Thank you to the instructor and GCP for this.

It was really a nice experience learning directly under people working for Google. I am really happy for the knowledge regarding the fundamentals of Google Cloud Platform Big Data and Machine Learning and got to know the importance of Data Management, Infrastructure Management and List of Features provided by Google Cloud such as BigQuery, DataProc, AppEngine etc to name a few. I would be proceeding further more into the Data Engineering Specialization Course.

Aaqib Ahmad

It is a great course, highly recommended for those who wants to work in the AI / Data science field or get a better understanding of these fast developing and highly sought after skills. It is very practical and designed for non-math background, so you do not need to have ample knowledge in probability, statistics, calculus to complete the course, although basic skills including programming and linear algebra are the key contributors of smooth completion of the course.

Good Starter Course -How we are using Google Cloud Platform & Infrastructure for Big Data and ML. There are very good example and practical session it is good to understand How google is processing structure data, streaming data and unstructured data.

This is course is only for a beginners. Those want to get deep dive into each and every Google Cloud data engineering products requested to look into "Preparing for Cloud Certification: Cloud Data Engineer" course.

Very concise and thorough course. I wasn't sure my knowledge of SQL and Python would be up to par, but it was plenty to complete the material. It's a good overview of the basics of GCP and how to utilize it effectively. Lots of real-world examples. Definitely a good starting point for someone who has learned some data analysis, some data science, and would like a better understanding of how to apply those concepts to large-scale data utilizing cloud products.

I could have got basic knowledge of GCP and how to manipulate it. Through this course, I have understood how useful it is and where I should use it. If I took this course in student life then I didn't struggle dealing with a big data. I faced some problems when tackling the labs. However, the support team kindly solved them soon. I would like to proceed to the next course. Thank you for providing the excellent cloud and machine learning course!

The course structure was really well set out. The video instructors were knowledgeable and easy to listen to. They are clearly very experienced in their domains. The pacing was really nice to keep up with and the labs gave some really nice hands on experience with otherwise expensive GCP technologies. The only criticism I have is that some of the instructions in the labs are not 100% accurate but it isn't anything that becomes a hindrance. 5/5

Practical Examples in the lab are good practice to understand concepts and implement them live. Sometimes got lost in following the the instructions and not understanding the concept. However, could redo the exercises multiple times that helped gain confidence and a good practice at the same time. Very relevant examples covered throughout the course. Videos and enough information provided thru multiple resources - case study link and pdf docs.

This is a amazing course for starting up with Data Engineering arena. This course cover a lot of aspects of data science, gives a holistic overview and yet remains simple enough for starters in this area. GCP has a solid set of products and remains open to use many open-source technologies. Combining this with the magnitude of data and api's google has...this is the future to be in. Expect a huge number of Citizen data engineers in future !!!

Excellent introduction to Big Data and ML concepts in general, and specifically the GCP offerings. The instructor is clearly an expert in using these technologies and his style of instruction is lucid. This course was my first blush with very cool tech such as PySpark, TensorFlow, BigQuery, CloudDataLab and the Google ML APIs - all of which I hope to use in some capacity or the other thanks to basic understanding of their rudiments.

This is a very good course to understand the Google Cloud Platform. Also we learn about the GCP Products like DataFlow, DataProc, Pub/Sub, BigQuery, Cloud ML, etc. With this course, we will learn about how we can mange very very large amount of data and machine learning to analysis the data. I like this course very much. I will continue with its other courses to get more learning. Because with this course, we will get fundamentals.

Fantastic , learning, gave me to many ideas which i ca implement in my day to day job, the only thing is, need more practice, but thanks, I would surely recommend and share this course.

One ting more, had 2 issues, where the transcript did not match the qwik labs and had issues in performing the task, have stated both the error logs to the chat , hope you take acre of them, Vandana from chat support did acknowledged,

thanks!

The best part of this course was we got to do lot of hands-on lab activity with detailed explanation and steps. Machine learning fundamentals were explained very well on where and how to implement the algorithms. Google cloud platform services are very easy to understand and use. I am very excited to build and explore using tons of resources shared during the course on my own. Thank you to excellent team of Google trainers!

The course is well established and maintained. The only issue is that, its an introductory course to GCP and its various features, nothing else. There are hands on labs but its not that productive, you will just get to understand the google cloud framework and how it is done. Its more of a product advertisement than a skillful course. Its great for beginners to understand what is GCP and understand its various features!

Dr.Lakshmanan does a great job covering the wide range of products Google has in the Cloud space, with real hands-on working examples. The lab on ML with TensorFlow was particularly exciting ! This course helped me get a clear understanding of the space each tool occupies and when to use them. The introductory hands-on labs got me started and now I feel confident I can build upon them on my own. Thank you Dr.Lakshmanan.

Enjoyed learning so many features and functionalities of the Google Cloud Platform.

It is really refreshing to learn something new and also to know that there are so many resources which can just be stitched together to build anything needed... all without having to search/ scramble for software to be installed on local systems.

Looking forward for the rest of the courses and to immerse in the Google's product offering.