One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Este curso faz parte do Programa de cursos integrados Ciência de Dados
Aprendizagem Automática na Prática
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Informações sobre o curso
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O que você vai aprender
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Use the basic components of building and applying prediction functions
Habilidades que você terá
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Programa - O que você aprenderá com este curso
Semana
12 horas para concluir
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 vídeos (Total 73 mín.), 3 leituras, 1 teste
9 videos
What is prediction?8min
Relative importance of steps9min
In and out of sample errors6min
Prediction study design9min
Types of errors10min
Receiver Operating Characteristic5min
Cross validation8min
What data should you use?6min
3 leituras
Welcome to Practical Machine Learning10min
Syllabus10min
Pre-Course Survey10min
1 exercício prático
Quiz 110min
Semana
22 horas para concluir
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 vídeos (Total 96 mín.), 1 teste
9 videos
Caret package6min
Data slicing5min
Training options7min
Plotting predictors10min
Basic preprocessing10min
Covariate creation17min
Preprocessing with principal components analysis14min
Predicting with Regression12min
Predicting with Regression Multiple Covariates11min
1 exercício prático
Quiz 210min
Semana
31 hora para concluir
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
This week we introduce a number of machine learning algorithms you can use to complete your course project.
5 vídeos (Total 48 mín.), 1 teste
5 videos
Bagging9min
Random Forests6min
Boosting7min
Model Based Prediction11min
1 exercício prático
Quiz 310min
Semana
44 horas para concluir
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 vídeos (Total 33 mín.), 2 leituras, 3 testes
4 videos
Combining predictors7min
Forecasting7min
Unsupervised Prediction4min
2 leituras
Course Project Instructions (READ FIRST)10min
Post-Course Survey10min
2 exercícios práticos
Quiz 410min
Course Project Prediction Quiz40min
40%
comecei uma nova carreira após concluir estes cursos
39%
consegui um benefício significativo de carreira com este curso
13%
recebi um aumento ou promoção
Principais avaliações do Aprendizagem Automática na Prática
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
Instrutores
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Sobre Universidade Johns Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Sobre Programa de cursos integrados Ciência de Dados
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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