Machine Learning

Este curso forma parte de Programa Especializado - Data Science

Practical Machine Learning

ofrecido por

Programa especializado Data Science

Johns Hopkins University

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....

Curso 8 de 10 en

Programa especializado Data Science

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Sugerido: 4 hours/week

Aprox. 13 horas para completar

English

Subtítulos: English

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

Curso 8 de 10 en

Programa especializado Data Science

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Sugerido: 4 hours/week

Aprox. 13 horas para completar

English

Subtítulos: English

Programa - Qué aprenderás en este curso

1

Sección

2 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 min), 3 readings, 1 quiz

9 videos

Prediction motivation8m

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

2

Sección

2 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 min), 1 quiz

9 videos

Caret package6m

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

3

Sección

1 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 min), 1 quiz

5 videos

Predicting with trees12m

Bagging9m

Random Forests6m

Boosting7m

Model Based Prediction11m

1 ejercicio de práctica

Quiz 310m

4

Sección

4 horas para completar

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors. ...

4 videos (Total: 33 min), 2 readings, 3 quizzes

4 videos

Regularized regression13m

Combining predictors7m

Forecasting7m

Unsupervised Prediction4m

2 lecturas

Course Project Instructions (READ FIRST)10m

Post-Course Survey10m

2 ejercicios de práctica

Quiz 410m

Course Project Prediction Quiz40m

Instructores

Jeff Leek, PhD

Associate Professor, Biostatistics

Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics

Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics

Bloomberg School of Public Health

Acerca de Johns Hopkins University

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 Data Science

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

When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?

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Practical Machine Learning

Curso 8 de 10 en

Cursos 100 % en línea

Fechas límite flexibles

Sugerido: 4 hours/week

English

Qué aprenderás

Habilidades que obtendrás

Curso 8 de 10 en

Cursos 100 % en línea

Fechas límite flexibles

Sugerido: 4 hours/week

English

Programa - Qué aprenderás en este curso

1

Week 1: Prediction, Errors, and Cross Validation

2

Week 2: The Caret Package

3

Week 3: Predicting with trees, Random Forests, & Model Based Predictions

4

Week 4: Regularized Regression and Combining Predictors

34%

83%

14%

Principales revisiones

Instructores

Jeff Leek, PhD

Roger D. Peng, PhD

Brian Caffo, PhD

Acerca de Johns Hopkins University

Acerca del programa especializado Data Science

Preguntas Frecuentes