Practical Machine Learning
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.
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- Level Foundation
- المدة 9 ساعات hours
- الطبع بواسطة Johns Hopkins University
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Offered by
عن
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.الوحدات
Week 1
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9
Videos
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4
Readings
9
Videos
- Prediction motivation
- What is prediction?
- Relative importance of steps
- In and out of sample errors
- Prediction study design
- Types of errors
- Receiver Operating Characteristic
- Cross validation
- What data should you use?
4
Readings
- Welcome to Practical Machine Learning
- A Note of Explanation
- Syllabus
- Pre-Course Survey
Week 1 Quiz
-
1
Assignment
1
Assignment
- Quiz 1
Week 2
-
9
Videos
9
Videos
- Caret package
- Data slicing
- Training options
- Plotting predictors
- Basic preprocessing
- Covariate creation
- Preprocessing with principal components analysis
- Predicting with Regression
- Predicting with Regression Multiple Covariates
Week 2 Quiz
-
1
Assignment
1
Assignment
- Quiz 2
Week 3
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5
Videos
5
Videos
- Predicting with trees
- Bagging
- Random Forests
- Boosting
- Model Based Prediction
Week 3 Quiz
-
1
Assignment
1
Assignment
- Quiz 3
Week 4
-
4
Videos
4
Videos
- Regularized regression
- Combining predictors
- Forecasting
- Unsupervised Prediction
Week 4 Quiz
-
1
Assignment
1
Assignment
- Quiz 4
Course Project
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1
Readings
-
1
PeerReview
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1
Assignment
1
Assignment
- Course Project Prediction Quiz
1
Peer Review
- Prediction Assignment Writeup
1
Readings
- Course Project Instructions (READ FIRST)
Share Your Feedback
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1
Readings
1
Readings
- Post-Course Survey
Auto Summary
Unlock the essentials of machine learning with "Practical Machine Learning" on Coursera. This foundational course, taught by experts in Data Science & AI, focuses on real-world applications of prediction functions. Learn key concepts like training sets, overfitting, error rates, and explore methods such as regression, classification trees, Naive Bayes, and random forests. Perfect for data scientists and analysts, the course spans 540 minutes and offers a Starter subscription option. Join now to enhance your predictive modeling skills!
Jeff Leek, PhD
Roger D. Peng, PhD
Brian Caffo, PhD