Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.
Offert par
Google CloudÀ propos de ce cours
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Certificat partageable
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100 % en ligne
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Niveau intermédiaire
Approx. 22 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
TensorflowBigqueryMachine LearningData Cleansing
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Approx. 22 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Google Cloud
We help 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.
Programme du cours : ce que vous apprendrez dans ce cours
8 minutes pour terminer
Introduction to Course
In this course you’ll get foundational ML knowledge so that you understand the terminology that we use throughout the specialization. You will also learn practical tips and pitfalls from ML practitioners here at Google and walk away with the code and the knowledge to bootstrap your own ML models.
8 minutes pour terminer
2 vidéos (Total 8 min)
4 heures pour terminer
Improve Data Quality and Exploratory Data Analysis
In this module, we will introduce data quality issues and how to improve them. We will then look at exploratory data anlaysis.
4 heures pour terminer
5 vidéos (Total 29 min), 1 lecture, 4 quiz
8 heures pour terminer
Practical ML
In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that you can accelerate your growth as an ML practitioner.
8 heures pour terminer
13 vidéos (Total 63 min), 1 lecture, 11 quiz
3 heures pour terminer
Optimization
In this module we will walk you through how to optimize your ML models.
3 heures pour terminer
13 vidéos (Total 61 min), 1 lecture, 4 quiz
4 heures pour terminer
Generalization and Sampling
Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.
4 heures pour terminer
9 vidéos (Total 64 min), 1 lecture, 4 quiz
3 heures pour terminer
Summary
3 heures pour terminer
1 vidéo (Total 4 min), 3 lectures, 1 quiz
Avis
Meilleurs avis pour LAUNCHING INTO MACHINE LEARNING
par OD30 mai 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.
par PT1 déc. 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 :)
par PA3 août 2018
Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.
par CR12 août 2021
Interesting course, and the technical details during week 3 and 4 were highly appreicated. The labs could have been a bit better put together, but all round happy with this course!
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