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： 约翰霍普金斯大学
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 视频, 3 阅读材料
- Reading: Welcome to Practical Machine Learning
- Reading: Syllabus
- Reading: Pre-Course Survey
- 视频: Prediction motivation
- 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 视频
- 视频: Caret package
- 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 视频
- 视频: Predicting with trees
- 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 视频, 2 阅读材料
- 视频: Regularized regression
- 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
约翰霍普金斯大学
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
Thank you! My teacher. The course very good. Many thanks
nice
Great class. Easy to follow and I learned a great deal.
Very interesting course, materials are explained in an engaging manner. I would have loved to have a few more exercises to practice, but overall a good course to understand the most important concepts of ML.