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.
Este curso faz parte do Programa de cursos integrados Machine Learning with TensorFlow on Google Cloud Platform
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Informações sobre o curso
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Resultados de carreira do aprendiz
40%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
24%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 20 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
TensorflowBigqueryMachine LearningData Cleansing
Resultados de carreira do aprendiz
40%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
24%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 20 horas para completar
Inglês
Legendas: Inglês
oferecido por
Google Cloud
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Programa - O que você aprenderá com este curso
8 minutos para concluir
Introduction to Course
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.
8 minutos para concluir
2 vídeos (Total 8 mín.)
4 horas para concluir
Improve Data Quality and Exploratory Data Analysis
In this module, we will introduce data quality issues and how to improve them. We will then look at exploratory data anlaysis.
4 horas para concluir
5 vídeos (Total 29 mín.), 1 leitura, 4 testes
5 videos
Introduction45s
Improve Data Quality13min
Lab Intro Improve Data Quality1min
Exploratory Data Anlaysis13min
Lab Intro Exploratory Data Analysis46s
1 leituras
Resources10min
2 exercícios práticos
Practice Quiz on Improve Data Quality30min
Practice Quiz on Exploratory Data Analysis30min
8 horas para concluir
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.
8 horas para concluir
13 vídeos (Total 63 mín.), 1 leitura, 11 testes
13 videos
Introduction1min
Supervised Learning5min
Regression and Classification11min
Short History of ML: Linear Regression7min
Short History of ML: Perceptron5min
Short History of ML: Neural Networks7min
Lab Intro: Introduction to Linear Regression26s
Lab Intro: Introduction to Logistic Regression21s
Short History of ML: Decision Trees5min
Short History of ML: Random Forests4min
Lab Intro: Decision Trees and Random Forests in Python30s
Short History of ML: Kernel Methods4min
Short History of ML: Modern Neural Networks8min
1 leituras
Resources10min
8 exercícios práticos
Supervised Learning30min
Regression and Classification30min
Linear Regression30min
Perceptron30min
Neural Networks30min
Decision Trees 30min
Kernel Methods30min
History of ML: Modern Neural Networks30min
3 horas para concluir
Optimization
In this module we will walk you through how to optimize your ML models.
3 horas para concluir
13 vídeos (Total 61 mín.), 1 leitura, 4 testes
13 videos
Introduction42s
Defining ML Models4min
Introducing the Course Dataset6min
Introduction Loss Functions6min
Gradient Descent5min
Troubleshooting Loss Curves2min
ML Model Pitfalls6min
Lecture Lab: Introducing the TensorFlow Playground6min
Lecture Lab: TensorFlow Playground - Advanced3min
Lecture Lab: Practicing with Neural Networks6min
Loss Curve Troubleshooting2min
Performance Metrics3min
Confusion Matrix5min
1 leituras
Resources10min
4 exercícios práticos
Lesson Quiz 30min
Lesson Quiz30min
Lesson Quiz30min
Module Quiz30min
4 horas para concluir
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. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.
4 horas para concluir
9 vídeos (Total 64 mín.), 1 leitura, 4 testes
9 videos
Introduction1min
Generalization and ML Models6min
When to Stop Model Training5min
Lecture Creating Repeatable Samples in BigQuery6min
LectureDemo: Splitting Datasets in BigQuery8min
Lab Introduction Creating Repeatable Dataset Splits in BigQuery1min
Lab Solution Walkthrough Creating Repeatable Dataset Splits in BigQuery9min
Lab Introduction Exploring and Creating ML Datasets2min
Lab Solution Walkthrough Exploring and Creating ML Datasets23min
1 leituras
Resources10min
2 exercícios práticos
Generalization and ML Models30min
Module Quiz30min
1 hora para concluir
Summary
1 hora para concluir
1 vídeo (Total 4 mín.), 2 leituras, 1 teste
1 vídeos
Course Summary3min
2 leituras
Resources - Readings Compiled as PDF10min
Quiz Questions as a PDF10min
1 exercício prático
Course Quiz30min
Avaliações
Principais avaliações do LAUNCHING INTO MACHINE LEARNING
por OD30 de Mai de 2020
Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging. Hope to see the instructor soon in a another course. Thanks so much. I learned a lot.
por PT1 de Dez de 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 :)
por PA3 de Ago de 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.
por KT8 de Abr de 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.
Sobre Programa de cursos integrados Machine Learning with TensorFlow on Google Cloud Platform
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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