Этот курс входит в специализацию ''Специализация Data Science'

Practical Machine Learning

от партнера

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....

Course 8 of 10 in the

специализации ''Data Science''

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Предполагаемая нагрузка: 4 hours/week

Прибл. 13 ч. на завершение

English

Субтитры: English

Чему вы научитесь

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

Приобретаемые навыки

Machine LearningMachine Learning AlgorithmsRandom ForestR Programming

Course 8 of 10 in the

специализации ''Data Science''

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Предполагаемая нагрузка: 4 hours/week

Прибл. 13 ч. на завершение

English

Субтитры: English

Программа курса: что вы изучите

1

Раздел

2 ч. на завершение

Week 1: Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation....

9 видео (всего 73 мин.), 3 материалов для самостоятельного изучения, 1 тест

9 видео

Prediction motivation8мин

What is prediction?8мин

Relative importance of steps9мин

In and out of sample errors6мин

Prediction study design9мин

Types of errors10мин

Receiver Operating Characteristic5мин

Cross validation8мин

What data should you use?6мин

3 материала для самостоятельного изучения

Welcome to Practical Machine Learning10мин

Syllabus10мин

Pre-Course Survey10мин

1 практическое упражнение

Quiz 110мин

2

Раздел

2 ч. на завершение

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing....

9 видео (всего 96 мин.), 1 тест

9 видео

Caret package6мин

Data slicing5мин

Training options7мин

Plotting predictors10мин

Basic preprocessing10мин

Covariate creation17мин

Preprocessing with principal components analysis14мин

Predicting with Regression12мин

Predicting with Regression Multiple Covariates11мин

1 практическое упражнение

Quiz 210мин

3

Раздел

1 ч. на завершение

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 видео (всего 48 мин.), 1 тест

5 видео

Predicting with trees12мин

Bagging9мин

Random Forests6мин

Boosting7мин

Model Based Prediction11мин

1 практическое упражнение

Quiz 310мин

4

Раздел

4 ч. на завершение

Week 4: Regularized Regression and Combining Predictors

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

4 видео (всего 33 мин.), 2 материалов для самостоятельного изучения, 3 тестов

4 видео

Regularized regression13мин

Combining predictors7мин

Forecasting7мин

Unsupervised Prediction4мин

2 материала для самостоятельного изучения

Course Project Instructions (READ FIRST)10мин

Post-Course Survey10мин

2 практического упражнения

Quiz 410мин

Course Project Prediction Quiz40мин

Преподавателя

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

О 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....

О специализации ''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....

Часто задаваемые вопросы

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

Course 8 of 10 in the

Только онлайн-курсы

Гибкие сроки

Предполагаемая нагрузка: 4 hours/week

English

Чему вы научитесь

Приобретаемые навыки

Course 8 of 10 in the

Только онлайн-курсы

Гибкие сроки

Предполагаемая нагрузка: 4 hours/week

English

Программа курса: что вы изучите

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%

Лучшие рецензии

Преподавателя

Jeff Leek, PhD

Roger D. Peng, PhD

Brian Caffo, PhD

О Johns Hopkins University

О специализации ''Data Science'

Часто задаваемые вопросы