***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***
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.
Course Objectives:
Identify why deep learning is currently popular
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets
SPECIALIZATION COMPLETION CHALLENGE
As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Machine Learning with TensorFlow on Google Cloud Platform’ specialization.
Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization).
You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles.
Your challenge awaits – begin learning on Coursera today!
Ce cours fait partie de la Spécialisation Machine Learning with TensorFlow on Google Cloud Platform
Launching into Machine Learning
proposé par
À propos de ce cours
4.5
1,536 notes
Compétences que vous acquerrez
TensorflowBigqueryMachine LearningData Cleansing
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
19 minutes pour terminer
Introduction
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.
2 vidéos (Total 9 min)
1 heure 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.
10 vidéos (Total 62 min), 1 quiz
10 vidéos
Introduction1 min
Supervised Learning5 min
Regression and Classification11 min
Short History of ML: Linear Regression7 min
Short History of ML: Perceptron5 min
Short History of ML: Neural Networks7 min
Short History of ML: Decision Trees5 min
Short History of ML: Kernel Methods4 min
Short History of ML: Random Forests4 min
Short History of ML: Modern Neural Networks8 min
1 exercice pour s'entraîner
Module Quiz6 min
1 heure pour terminer
Optimization
In this module we will walk you through how to optimize your ML models.
13 vidéos (Total 60 min), 1 quiz
13 vidéos
Introduction42s
Defining ML Models4 min
Introducing the Natality Dataset6 min
Introducing Loss Functions6 min
Gradient Descent5 min
Troubleshooting a Loss Curve2 min
ML Model Pitfalls6 min
Lab: Introducing the TensorFlow Playground6 min
Lab: TensorFlow Playground - Advanced3 min
Lab: Practicing with Neural Networks6 min
Loss Curve Troubleshooting1 min
Performance Metrics3 min
Confusion Matrix5 min
1 exercice pour s'entraîner
Module Quiz6 min
3 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.
9 vidéos (Total 64 min), 3 quiz
9 vidéos
Introduction1 min
Generalization and ML Models6 min
When to Stop Model Training5 min
Creating Repeatable Samples in BigQuery6 min
Demo: Splitting Datasets in BigQuery8 min
Lab Introduction1 min
Lab Solution Walkthrough9 min
Lab Introduction2 min
Lab Solution Walkthrough23 min
1 exercice pour s'entraîner
Module Quiz12 min
4.5
157 avis53%
a commencé une nouvelle carrière après avoir terminé ces cours
47%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
24%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
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 :)
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.
À propos de 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.
À propos de la Spécialisation Machine Learning with TensorFlow on Google Cloud Platform
>>>Look for details below for COMPLETION CHALLENGE, receive up to $150 in Qwiklabs credits<<<
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.
Looking to make a career change? Upon completion of this Specialization, you’ll have the opportunity to share your information directly with Google and Publicis to be considered for open hiring opportunities.
COMPLETION CHALLENGE
For every course you complete before May 5, 2019, we will send you 30 Qwiklabs credits (upto $150 USD value)!
> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <
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