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
이 강좌는 Machine Learning with TensorFlow on Google Cloud Platform 전문 분야의 일부입니다.
Launching into Machine Learning
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Course을(를) 수강하는 학습자
- Data Scientists
- Machine Learning Engineers
- Biostatisticians
- Chief Technology Officers (CTOs)
- Data Engineers
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 9분 필요
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.
2개 동영상 (총 9분)
완료하는 데 1시간 필요
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.
10개 동영상 (총 62분), 1 quiz
10개의 동영상
Supervised Learning5m
Regression and Classification11m
Short History of ML: Linear Regression7m
Short History of ML: Perceptron5m
Short History of ML: Neural Networks7m
Short History of ML: Decision Trees5m
Short History of ML: Kernel Methods4m
Short History of ML: Random Forests4m
Short History of ML: Modern Neural Networks8m
1개 연습문제
Module Quiz6m
완료하는 데 1시간 필요
Optimization
In this module we will walk you through how to optimize your ML models.
13개 동영상 (총 60분), 1 quiz
13개의 동영상
Defining ML Models4m
Introducing the Natality Dataset6m
Introducing Loss Functions6m
Gradient Descent5m
Troubleshooting a Loss Curve2m
ML Model Pitfalls6m
Lab: Introducing the TensorFlow Playground6m
Lab: TensorFlow Playground - Advanced3m
Lab: Practicing with Neural Networks6m
Loss Curve Troubleshooting1m
Performance Metrics3m
Confusion Matrix5m
1개 연습문제
Module Quiz6m
완료하는 데 3시간 필요
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.
9개 동영상 (총 64분), 3 quizzes
9개의 동영상
Generalization and ML Models6m
When to Stop Model Training5m
Creating Repeatable Samples in BigQuery6m
Demo: Splitting Datasets in BigQuery8m
Lab Introduction1m
Lab Solution Walkthrough9m
Lab Introduction2m
Lab Solution Walkthrough23m
1개 연습문제
Module Quiz12m
4.6
317개의 리뷰45%
이 강좌를 수료한 후 새로운 경력 시작하기
46%
이 강좌를 통해 확실한 경력상 이점 얻기
29%
급여 인상 또는 승진하기
Launching into Machine Learning의 최상위 리뷰
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.
Google 클라우드 정보
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Machine Learning with TensorFlow on Google Cloud Platform 전문 분야 정보
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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