Practical Machine Learning
link 来源:www.coursera.org
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assignment 等级:入门
chat_bubble_outline 语言:英语
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关于内容

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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课程大纲

  • Week 1 - Week 1: Prediction, Errors, and Cross Validation
    This week will cover prediction, relative importance of steps, errors, and cross validation.
  • Week 2 - Week 2: The Caret Package
    This week will introduce the caret package, tools for creating features and preprocessing.
  • Week 3 - 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.
  • Week 4 - Week 4: Regularized Regression and Combining Predictors
    This week, we will cover regularized regression and combining predictors.
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教师

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

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内容设计师

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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平台

Coursera

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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3.6 /5 平均值
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3.6/5
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最佳评论

A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

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发布日期2018年2月22日
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2018年2月22日
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A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

2018年2月21日
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Unsatisfactory and poor course in this specialisation. There are many important parts which are explained inaccurately. In many cases, the lecturer jumps from important points, or assumes students have detailed knowledge about the topic. You can find ambiguity in weekly questions. Very unsatisfied!

2018年2月13日
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I was rather disappointed with this course. I guess it fills the objective of getting you using the caret package and getting you started with some examples. However to understand what you are doing you should defintively go somewhere else. I definitively missed some swirl exercises and more flow diagrams in the slides. It felt for me as I was just copypasting some code from the slides. The course does clearly give some good literature and places to go for details.

2018年2月12日
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Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

2018年2月4日
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The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.