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.
ofrecido por
Universidad Johns Hopkins
Acerca de este Curso
64,299 vistas recientes
Qué aprenderás
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 obtendrás
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Los estudiantes que toman este Course son
- Risk Managers
- Biostatisticians
- Data Scientists
- Data Analysts
- Biologists
Programa - Qué aprenderás en este curso
Semana
12 horas para completar
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 videos (Total 73 minutos), 4 lecturas, 1 cuestionario
9 videos
What is prediction?8m
Relative importance of steps9m
In and out of sample errors6m
Prediction study design9m
Types of errors10m
Receiver Operating Characteristic5m
Cross validation8m
What data should you use?6m
4 lecturas
Welcome to Practical Machine Learning10m
A Note of Explanation2m
Syllabus10m
Pre-Course Survey10m
1 ejercicio de práctica
Quiz 110m
Semana
22 horas para completar
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 videos (Total 96 minutos), 1 cuestionario
9 videos
Data slicing5m
Training options7m
Plotting predictors10m
Basic preprocessing10m
Covariate creation17m
Preprocessing with principal components analysis14m
Predicting with Regression12m
Predicting with Regression Multiple Covariates11m
1 ejercicio de práctica
Quiz 210m
Semana
31 hora para completar
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 videos (Total 48 minutos), 1 cuestionario
1 ejercicio de práctica
Quiz 310m
Semana
44 horas para completar
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 videos (Total 33 minutos), 2 lecturas, 3 cuestionarios
2 lecturas
Course Project Instructions (READ FIRST)10m
Post-Course Survey10m
2 ejercicios de práctica
Quiz 410m
Course Project Prediction Quiz40m
39%
comenzó una nueva carrera después de completar estos cursos
38%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
Principales revisiones sobre Practical Machine Learning
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.
Instructores
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
Acerca de Universidad 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.
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