Об этом курсе
4.5
2,027 ratings
402 reviews
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....
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Начните сейчас и учитесь по собственному графику.
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Гибкие сроки

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Предполагаемая нагрузка: 4 hours/week

Прибл. 13 ч. на завершение
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English

Субтитры: English

Чему вы научитесь

  • Check
    Describe machine learning methods such as regression or classification trees
  • Check
    Explain the complete process of building prediction functions
  • Check
    Understand concepts such as training and tests sets, overfitting, and error rates
  • Check
    Use the basic components of building and applying prediction functions

Приобретаемые навыки

Machine LearningMachine Learning AlgorithmsRandom ForestR Programming
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 4 hours/week

Прибл. 13 ч. на завершение
Comment Dots

English

Субтитры: English

Программа курса: что вы изучите

1

Раздел
Clock
2 ч. на завершение

Week 1: Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation....
Reading
9 видео (всего 73 мин.), 3 материалов для самостоятельного изучения, 1 тест
Video9 видео
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
Reading3 материала для самостоятельного изучения
Welcome to Practical Machine Learning10мин
Syllabus10мин
Pre-Course Survey10мин
Quiz1 практическое упражнение
Quiz 110мин

2

Раздел
Clock
2 ч. на завершение

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing....
Reading
9 видео (всего 96 мин.), 1 тест
Video9 видео
Data slicing5мин
Training options7мин
Plotting predictors10мин
Basic preprocessing10мин
Covariate creation17мин
Preprocessing with principal components analysis14мин
Predicting with Regression12мин
Predicting with Regression Multiple Covariates11мин
Quiz1 практическое упражнение
Quiz 210мин

3

Раздел
Clock
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....
Reading
5 видео (всего 48 мин.), 1 тест
Video5 видео
Bagging9мин
Random Forests6мин
Boosting7мин
Model Based Prediction11мин
Quiz1 практическое упражнение
Quiz 310мин

4

Раздел
Clock
4 ч. на завершение

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors. ...
Reading
4 видео (всего 33 мин.), 2 материалов для самостоятельного изучения, 3 тестов
Video4 видео
Combining predictors7мин
Forecasting7мин
Unsupervised Prediction4мин
Reading2 материала для самостоятельного изучения
Course Project Instructions (READ FIRST)10мин
Post-Course Survey10мин
Quiz2 практического упражнения
Quiz 410мин
Course Project Prediction Quiz40мин
4.5
Direction Signs

34%

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Briefcase

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получил значимые преимущества в карьере благодаря этому курсу
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Лучшие рецензии

автор: ADMar 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.

автор: ASAug 31st 2017

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.

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....
Data Science

Часто задаваемые вопросы

  • 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.

  • 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.

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