关于此课程： 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.
制作方： Johns Hopkins University
教学方： Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
教学方： Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
教学方： Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| 基本信息 | 课程 8（共 10 门，Data Science Specialization ） |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 个视频, 3 个阅读材料
- Reading: Welcome to Practical Machine Learning
- Reading: Syllabus
- Reading: Pre-Course Survey
- 视频: What is prediction?
- 视频: Relative importance of steps
- 视频: In and out of sample errors
- 视频: Prediction study design
- 视频: Types of errors
- 视频: Receiver Operating Characteristic
- 视频: Cross validation
- 视频: What data should you use?
已评分: Quiz 1
第 2 周
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 个视频
- 视频: Data slicing
- 视频: Training options
- 视频: Plotting predictors
- 视频: Basic preprocessing
- 视频: Covariate creation
- 视频: Preprocessing with principal components analysis
- 视频: Predicting with Regression
- 视频: Predicting with Regression Multiple Covariates
已评分: Quiz 2
第 3 周
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 个视频
- 视频: Bagging
- 视频: Random Forests
- 视频: Boosting
- 视频: Model Based Prediction
已评分: Quiz 3
第 4 周
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 个视频, 2 个阅读材料
- 视频: Combining predictors
- 视频: Forecasting
- 视频: Unsupervised Prediction
- Reading: Course Project Instructions (READ FIRST)
- Reading: Post-Course Survey
已评分: Quiz 4
已评分: Prediction Assignment Writeup
已评分: Course Project Prediction Quiz
常见问题解答
运作方式
Coursework
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制作方
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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评分和审阅
Great class! I wish you would do a little more explanation about what methods are best for which scenarios. If you did in fact explain that and it went over my head or I missed it, I apologize. Great class that I learned a lot from.
Very interesting course. May be a little bit harder than the previous ones but it could be done.
Good course, I miss more practice exercise because theory is always welcome but when we are capable to understand is doing real practice.
SB
I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.
I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?
Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.