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
本课程是 Machine Learning with TensorFlow on Google Cloud Platform 专项课程 专项课程的一部分
Launching into Machine Learning
提供方
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 9 分钟
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 个视频 （总计 9 分钟）
完成时间为 1 小时
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 个视频 （总计 62 分钟）, 1 个测验
10 个视频
Introduction1分钟
Short History of ML: Linear Regression7分钟
Short History of ML: Perceptron5分钟
Short History of ML: Neural Networks7分钟
Short History of ML: Decision Trees5分钟
Short History of ML: Kernel Methods4分钟
Short History of ML: Random Forests4分钟
Short History of ML: Modern Neural Networks8分钟
1 个练习
Module Quiz6分钟
完成时间为 1 小时
Optimization
In this module we will walk you through how to optimize your ML models.
13 个视频 （总计 60 分钟）, 1 个测验
13 个视频
Introducing Loss Functions6分钟
Gradient Descent5分钟
Troubleshooting a Loss Curve2分钟
ML Model Pitfalls6分钟
Lab: Introducing the TensorFlow Playground6分钟
Lab: TensorFlow Playground - Advanced3分钟
Lab: Practicing with Neural Networks6分钟
Loss Curve Troubleshooting1分钟
Performance Metrics3分钟
Confusion Matrix5分钟
1 个练习
Module Quiz6分钟
完成时间为 3 小时
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 个视频 （总计 64 分钟）, 3 个测验
9 个视频
Introduction1分钟
Creating Repeatable Samples in BigQuery6分钟
Demo: Splitting Datasets in BigQuery8分钟
Lab Introduction1分钟
Lab Solution Walkthrough9分钟
Lab Introduction2分钟
Lab Solution Walkthrough23分钟
1 个练习
Module Quiz12分钟
4.6
280 个审阅48%
完成这些课程后已开始新的职业生涯
44%
通过此课程获得实实在在的工作福利
25%
加薪或升职
来自Launching into Machine Learning的热门评论
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.
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关于 Machine Learning with TensorFlow on Google Cloud Platform 专项课程
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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