Key Information
About the content
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.
Syllabus
- 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.
Instructors
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
Content Designer
Johns Hopkins University (JHU) is a private American university located in Baltimore, Maryland. It also has campuses in Washington, D.C. Bologna, Italy, Singapore and Nanjing, China. It owes its name to Johns Hopkins, a wealthy entrepreneur who bequeathed 7 million dollars to the university on his death.
One of the most prestigious universities in the United States (especially for its faculties of medicine and public health, as well as its school of international affairs), the institution defines itself as the country's leading "research university". At the beginning of its history, it was mainly inspired by the University of Heidelberg and the German educational model of Wilhelm von Humboldt. In 2019, 39 Nobel Prize winners have their names associated with the university.
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
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.
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.
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!
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.
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.
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.
