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.
이 강좌는 Machine Learning with TensorFlow on Google Cloud Platform 전문 분야의 일부입니다.
Launching into Machine Learning
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About this Course
최근 조회 32,151회
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귀하가 습득할 기술
TensorflowBigqueryMachine LearningData Cleansing
학습자 경력 결과
43%
이 강좌를 수료한 후 새로운 경력 시작하기
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 2번째 강좌:
유동적 마감일
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중급 단계
완료하는 데 약 7시간 필요
권장: 5 - 7 hours per week
영어
자막: 프랑스어, 포르투갈어 (브라질), 독일어, 영어, 스페인어, 일본어
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강의 계획 - 이 강좌에서 배울 내용
완료하는 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.
완료하는 데 9분 필요
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.
완료하는 데 1시간 필요
10개 동영상 (총 62분)
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.
완료하는 데 1시간 필요
13개 동영상 (총 60분)
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.
완료하는 데 3시간 필요
9개 동영상 (총 64분)
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
검토
4.6
LAUNCHING INTO MACHINE LEARNING의 최상위 리뷰
PT 제공Dec 1, 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 :)
PA 제공Aug 3, 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.
KT 제공Apr 8, 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.
AR 제공Aug 16, 2019
I got a whole idea on how to work on data from scratch. Model selection, generalization, splitting of data and performance metric were few things I learned from this course.
PT 제공Jun 25, 2018
Overall it was great, and very instructive. However, the Short History of ML was a little bit confusing with too many unexplained words and too many details too early.
AM 제공Jul 13, 2018
Great presenter. High energy engaging. The material is more difficult and to develop intuition of why the sampling needs to result in constant RMSE didn't come across.
KH 제공Feb 12, 2019
This course gave me a good overview of how to work with GCP for ML and also helped in covering a bit of knowledge gaps that I had when I learnt things on my own.
SV 제공Dec 3, 2018
A great course to boost your confidence on practicing ML. It also teaches you some fresh skills like repeatable dataset partitioning techniques using just SQL.
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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