May 31, 2020
Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging. Hope to see the instructor soon in a another course. Thanks so much. I learned a lot.
Dec 02, 2018
This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)
By Kaustubh M H•
Feb 13, 2019
This course gave me a good overview of how to work with GCP for ML and also helped in covering a bit of knowledge gaps that I had when I learnt things on my own.
By SUJITH V•
Dec 04, 2018
A great course to boost your confidence on practicing ML. It also teaches you some fresh skills like repeatable dataset partitioning techniques using just SQL.
May 06, 2019
This course is very helpful to understand the machine learning concepts of various modals, splitting of the data and even training the model for benchmark.
By shashank s•
Apr 09, 2020
I highly recommend this course to learners who need an exposure on handling huge datasets using google big query SQL and data splitting strategies.
By Víctor D L T•
Apr 20, 2019
Excelente curso, muy recomendado para ampliar el entendimiento sobre Aprendizaje Automático, me gustó mucho haber podido usar Tensorflow Playground
By Ravindu R P•
Apr 27, 2020
Very useful course content and let me to identify some mistakes I done when I do experiments and learnt the how the benchmark phase really works
May 01, 2019
I learned machine learning well with this class. Thanks to Google for making these lecture films and allowing us to learn with these lecutres.
By Emre S•
Apr 29, 2018
The technical knowledge is introduced very progressively. You understand the historic evolution and practical usage of models. Great content!
By Mary B•
Aug 08, 2019
The math made me pull out old calc textbooks, but very good building of where the ML process is headed in terms of getting good sample data.
By Benjamin B•
Sep 05, 2018
Good course with a nice balance between general overview and the more granular aspects of ML. Looking forward to the next one in the series.
By Dilip T R•
Apr 27, 2020
Got to know about working very large datasets in GB size. The Tensorflow playground is really cool tool to understand parameter tunning.
By Juan P D P•
May 29, 2018
The teachers really try their best you understand the fundamentals giving you examples and showing in an easy way how you can do it.
By Harold M•
Sep 15, 2018
A very good introduction to ML and to important tools to use on the creating and tuning and optimization of ML models.
By Attila T•
Apr 27, 2019
Very good description of all the basic concepts of supervised learning problems. Many thanks for putting this content together!
By Shuo D•
Aug 28, 2018
Lab and the hand-on session were very useful! Also, the tip of using mod + rand() helps me to solve my own problem! Good job!
By Manabu K•
Jun 07, 2020
This course is so really good to learn about the general knowledge and skill of machine learning with Google Cloud Platform.
By Naqash B•
Oct 28, 2019
Very informative course. Enjoyed it end to end. Loved the way Evan explained everything. He is a super instructor. kudos!
By Christian C•
Aug 29, 2019
I love this course a lot. I learned so many things on how Google operates its google product services through ML and AI.
By Stefan K•
Apr 14, 2020
Excellent content - actually explained now NNs function with internal features sets. great labs to experiment
By Min L•
May 30, 2018
The course give a good introduction of machine learning and hands on exercise. It is practical and efficient.
By pravin b•
Sep 08, 2018
A good introduction to the process. Well structured. In particular I liked the section on loss functions.
By RAVI R K•
Jan 09, 2020
Nice course for getting started with Data Classification and High level knowledge on different models.
By Sachin K•
Jul 11, 2019
Really helped me to understand the nuts and bolts of ML and Data preparation along with preprocessing.
By Chong M T•
Mar 24, 2019
Mainly covering the principles of data selection and bucketing for training, validation and test sets.