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.
提供:
このコースについて
学習内容
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
習得するスキル
- Random Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
提供:

ジョンズ・ホプキンズ大学(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.
シラバス - 本コースの学習内容
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.
レビュー
- 5 stars66.42%
- 4 stars22.37%
- 3 stars6.92%
- 2 stars2.49%
- 1 star1.77%
実践的機械学習 からの人気レビュー
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
This was my favorite class of the specialization. It was taught very well, and I felt like everything I learned in the previous classes were finally coming together.
This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..
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