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.
Ce cours fait partie de la Spécialisation Machine Learning with TensorFlow on Google Cloud Platform
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À propos de ce cours
Résultats de carrière des étudiants
39%
ont commencé une nouvelle carrière après avoir terminé ce cours
37%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
23%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
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Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 5 dans le
Dates limites flexibles
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Niveau intermédiaire
Approx. 20 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
TensorflowBigqueryMachine LearningData Cleansing
Résultats de carrière des étudiants
39%
ont commencé une nouvelle carrière après avoir terminé ce cours
37%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
23%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 20 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Google Cloud
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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
1 heure pour terminer
Summary
1 heure pour terminer
1 vidéo (Total 4 min), 2 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 KT8 avr. 2019
My favourite course in the specialisation. I think it's a great idea to use historic time-frame to explain the advances in ML and why there is so much hype around deep learning.
À propos du Spécialisation Machine Learning with TensorFlow on Google Cloud Platform
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 features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
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