Practical Machine Learning

4.5
1,926 ratings
386 reviews

Course 8 of 10 in the Data Science Specialization

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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100% 在线课程

立即开始,按照自己的计划学习。
Clock

完成时间大约为13 小时

建议:4 hours/week
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English

字幕:English

您将学到的内容有

  • 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

您将获得的技能

Machine LearningRandom ForestR ProgrammingPredictive Modelling
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Clock

完成时间大约为13 小时

建议:4 hours/week
Comment Dots

English

字幕:English

Syllabus - What you will learn from this course

1

Section
Clock
2 hours to complete

Week 1: Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation....
Reading
9 videos (Total 73 min), 3 readings, 1 quiz
Video9 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
Reading3 readings
Welcome to Practical Machine Learning10m
Syllabus10m
Pre-Course Survey10m
Quiz1 practice exercises
Quiz 110m

2

Section
Clock
2 hours to complete

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing....
Reading
9 videos (Total 96 min), 1 quiz
Video9 videos
Data slicing5m
Training options7m
Plotting predictors10m
Basic preprocessing10m
Covariate creation17m
Preprocessing with principal components analysis14m
Predicting with Regression12m
Predicting with Regression Multiple Covariates11m
Quiz1 practice exercises
Quiz 210m

3

Section
Clock
1 hour to complete

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 videos (Total 48 min), 1 quiz
Video5 videos
Bagging9m
Random Forests6m
Boosting7m
Model Based Prediction11m
Quiz1 practice exercises
Quiz 310m

4

Section
Clock
4 hours to complete

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors. ...
Reading
4 videos (Total 33 min), 2 readings, 3 quizzes
Video4 videos
Combining predictors7m
Forecasting7m
Unsupervised Prediction4m
Reading2 readings
Course Project Instructions (READ FIRST)10m
Post-Course Survey10m
Quiz2 practice exercises
Quiz 410m
Course Project Prediction Quiz40m
4.5
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started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course
Money

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got a pay increase or promotion

Top Reviews

By JCJan 17th 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

By 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.

Instructors

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Jeff Leek, PhD

Associate Professor, Biostatistics
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Roger D. Peng, PhD

Associate Professor, Biostatistics
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Brian Caffo, PhD

Professor, Biostatistics

About 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....

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