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.
von
Über diesen Kurs
Was Sie lernen werden
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Kompetenzen, die Sie erwerben
- Random Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
von

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.
Lehrplan - Was Sie in diesem Kurs lernen werden
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
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: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
Bewertungen
- 5 stars66,44 %
- 4 stars22,36 %
- 3 stars6,93 %
- 2 stars2,49 %
- 1 star1,74 %
Top-Bewertungen von PRAKTISCHES MASCHINELLES LERNEN
This is a well thought about course which focuses on familiarizing the learner on the concepts of Machine Learning and develops a love in the learner towards predictive modeling. Thank you
Very good course. Clear explanations and examples give a good overview of the foundations of Machine Learning. After this course the student can build Machine Learning models.
It was like opening up a door to a whole new world. I have discovered new tools that I will thoroughly enjoy to use for the exploration of data and for predictions. Thanks Team Coursera !
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course
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