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.
ジョンズ・ホプキンズ大学（Johns Hopkins University）このコースについて
94,365 最近の表示
受講生の就業成果
38%
コース終了後に新しいキャリアをスタートした
38%
コースが具体的なキャリアアップにつながった
12%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
約8時間で修了
英語
字幕：英語, 韓国語
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 ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
受講生の就業成果
38%
コース終了後に新しいキャリアをスタートした
38%
コースが具体的なキャリアアップにつながった
12%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
約8時間で修了
英語
字幕：英語, 韓国語
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
2時間で修了
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
2時間で修了
9件のビデオ （合計73分）, 4 readings, 1 quiz
9件のビデオ
What is prediction?8 分
Relative importance of steps9 分
In and out of sample errors6 分
Prediction study design9 分
Types of errors10 分
Receiver Operating Characteristic5 分
Cross validation8 分
What data should you use?6 分
4件の学習用教材
Welcome to Practical Machine Learning10 分
A Note of Explanation2 分
Syllabus10 分
Pre-Course Survey10 分
1の練習問題
Quiz 110 分
2時間で修了
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
2時間で修了
9件のビデオ （合計96分）
9件のビデオ
Data slicing5 分
Training options7 分
Plotting predictors10 分
Basic preprocessing10 分
Covariate creation17 分
Preprocessing with principal components analysis14 分
Predicting with Regression12 分
Predicting with Regression Multiple Covariates11 分
1の練習問題
Quiz 210 分
1時間で修了
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.
1時間で修了
5件のビデオ （合計48分）
1の練習問題
Quiz 310 分
4時間で修了
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4時間で修了
4件のビデオ （合計33分）, 2 readings, 3 quizzes
2件の学習用教材
Course Project Instructions (READ FIRST)10 分
Post-Course Survey10 分
2の練習問題
Quiz 410 分
Course Project Prediction Quiz40 分
レビュー
PRACTICAL MACHINE LEARNING からの人気レビュー
ADにより2017年2月28日
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
ASにより2017年8月30日
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.
DHにより2018年6月17日
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.
EGにより2016年7月27日
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
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