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 forma parte de Programa Especializado - Ciencia de Datos
Practical Machine Learning
ofrecido por
Acerca de este Curso
4.5
2,330 calificaciones
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
Programa - Qué aprenderás en este curso
Semana
1Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 videos (Total 73 minutos), 3 readings, 1 quiz
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
3 lecturas
Welcome to Practical Machine Learning10m
Syllabus10m
Pre-Course Survey10m
1 ejercicio de práctica
Quiz 110m
Semana
2Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 videos (Total 96 minutos), 1 quiz
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
3Week 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 quiz
1 ejercicio de práctica
Quiz 310m
Semana
4Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 videos (Total 33 minutos), 2 readings, 3 quizzes
2 lecturas
Course Project Instructions (READ FIRST)10m
Post-Course Survey10m
2 ejercicios de práctica
Quiz 410m
Course Project Prediction Quiz40m
38%
comenzó una nueva carrera después de completar estos cursos
39%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
16%
consiguió un aumento de sueldo o ascenso
Principales revisiones
por AD•Mar 1st 2017
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.
por DH•Jun 18th 2018
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
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.
Acerca del programa especializado Ciencia de Datos
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.
Preguntas Frecuentes
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