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

( 75 Reviews )

Created by: Google Cloud Training

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Overall Score : 88 / 100

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
75 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.

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Reviews

4.4

75 total reviews

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By Jafed E on 6-Jul-19

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

By yu m c on 27-Jun-19

poor labs

By Mike W on 22-Jun-19

The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

By Arman A on 11-Apr-19

Pros: Tensorflow is an excellent framework for deep learningCons :1- The way this material is designed is 10 X SHIT2- Either teach properly or don't teach at all.

By Super-intelligent S o t C B on 14-Nov-19

Good course, but I couldn't get over the Estimator API. IMHO it's too complicated compared to Keras and I just could not force myself to care about it.

By Matthew B on 29-Jun-19

Labs were very confusing. Explained theories well but in practice didn't really learn much. I wouldn't recommend if you're a beginner. Google has a very interesting way on teaching.... On that note they should stick to building tech, never teaching. Didn't really learn how to build anything in ML, sort of skimmed on some API's they offer. In reality, the first course was probably the best... The rest of the specialization was just a rinse and repeat sort of thing.

By Rahul K on 5-May-19