Art and Science of Machine Learning

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to feature

( 135 Reviews )

Created by: Google Cloud Training

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Overall Score : 88 / 100

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Course Description

Welcome to the art and science of machine learning. In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.COMPLETION CHALLENGEComplete any GCP specialization from November 5 - November 30, 2019 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details.

Time: 8h

Instructor Details

Google Cloud Training

4.4 Rating
135 Reviews

The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps millions of organizations empower their employees, serve their customers, and build what's next for their businesses with innovative technology created in-and for-the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

Reviews

4.4

135 total reviews

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By Eric on 1-Feb-19

It has a lot fo great content, but the way it's laid out confused me greatly.It also felt very rushed at times, many parts weren't explained in detail, making many parts much less educational than I feel they should've been.The best parts are where Lak is presenting, even in the lab solutions he went to more details than anyone else and made even those videos interesting after solving it myself.

By Jun W on 30-May-18

This final course is also very good. Embedding is my favorite part and Lak is my favorite instructor.Thanks Googlers! Looking forward to the next five courses.

By Soham M on 12-Oct-18

The best course ever to give you the glimpse of the whole ML land - what is ML? Why ML? and most importantly How to do ML for real life business problems - from developing models to model evaluation, serving in production with all scaling tips and techniques with essence of Cloud. The best course for ML practitioners out there. Learnt a lot and what other place to learn from apart from the people who are actually doing it for the most popular products used across the web and mobile world.

By Luciano M on 13-Jan-19

Labs were had less quality than in previous weeks.

By Laimonas S on 2-Dec-18

Too shallow to truly be useful. I think if anything it gives you an idea of what's possible and roughly the areas you should explore and learn about but you won't learn too much following this through.

By Yakup K Y on 20-May-19

Some lab contents are distracting from the core subject, deviate from the video contents.

By Dmitry B on 6-May-19

The quality of the lesson material is great but the quantity is nowhere sufficient to get the hands-on experience

By john f d on 18-Jul-18

Labs vms are to slow. Speaker is difficult to understand. Mic varies and speech pattern is not clear. The presentations need some graphics rather than a guy talking. Sketch out the ideas on a white board rather than talking 5 minutes to a single slide.

By Jordan K R M on 26-Nov-18

Excelente

By Zezhou J on 24-Nov-18

Loved the breadth and depth of machine learning topics in this course.

By Pawan K T on 12-Dec-18

Great inside about the core topics with real word problems which make learning easier and practical. Thanks a lot for this great course

By Mario R on 13-Jan-19

Great course! You get some basics on how to fine tune your model (and why those methods are effective). Nice introduction to NN and what I think was the most relevant: building estimators from scratch and how Keras can offer a simpler way to work.

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