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
Этот курс входит в специализацию ''Специализация Machine Learning with TensorFlow on Google Cloud Platform'
Launching into Machine Learning
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Программа курса: что вы изучите
Неделя
1Introduction
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 видео (всего 9 мин.)
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 видео (всего 62 мин.), 1 тест
10 видео
Introduction1мин
Supervised Learning5мин
Regression and Classification11мин
Short History of ML: Linear Regression7мин
Short History of ML: Perceptron5мин
Short History of ML: Neural Networks7мин
Short History of ML: Decision Trees5мин
Short History of ML: Kernel Methods4мин
Short History of ML: Random Forests4мин
Short History of ML: Modern Neural Networks8мин
1 практическое упражнение
Module Quiz6мин
Optimization
In this module we will walk you through how to optimize your ML models.
13 видео (всего 61 мин.), 1 тест
13 видео
Introductionмин
Defining ML Models4мин
Introducing the Natality Dataset6мин
Introducing Loss Functions6мин
Gradient Descent5мин
Troubleshooting a Loss Curve2мин
ML Model Pitfalls6мин
Lab: Introducing the TensorFlow Playground6мин
Lab: TensorFlow Playground - Advanced3мин
Lab: Practicing with Neural Networks6мин
Loss Curve Troubleshooting1мин
Performance Metrics3мин
Confusion Matrix5мин
1 практическое упражнение
Module Quiz6мин
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 видео (всего 64 мин.), 3 тестов
9 видео
Introduction1мин
Generalization and ML Models6мин
When to Stop Model Training5мин
Creating Repeatable Samples in BigQuery6мин
Demo: Splitting Datasets in BigQuery8мин
Lab Introduction1мин
Lab Solution Walkthrough9мин
Lab Introduction2мин
Lab Solution Walkthrough23мин
1 практическое упражнение
Module Quiz12мин
4.5
Лучшие рецензии
автор: PA•Aug 4th 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.
автор: ES•Apr 29th 2018
The technical knowledge is introduced very progressively. You understand the historic evolution and practical usage of models. Great content!
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О специализации ''Machine Learning with TensorFlow on Google Cloud Platform'
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*Look for details below for COMPLETION CHALLENGE, receive a GCP t-shirt!*
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.
Complete any GCP specialization from now through November 30, 2018 for an opportunity to receive a GCP t-shirt (while supplies last). See forums for details.
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