About this Course

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Learner Career Outcomes

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Shareable Certificate
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Flexible deadlines
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Approx. 21 hours to complete
English

Skills you will gain

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

Learner Career Outcomes

46%

started a new career after completing these courses

48%

got a tangible career benefit from this course

19%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 21 hours to complete
English

Offered by

Placeholder

University of Washington

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(10,415 ratings)Info
Week
1

Week 1

1 hour to complete

Welcome!

1 hour to complete
8 videos (Total 27 min), 3 readings
8 videos
What is this course about?6m
Impact of classification1m
Course overview3m
Outline of first half of course5m
Outline of second half of course5m
Assumed background3m
Let's get started!45s
3 readings
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Reading: Software tools you'll need10m
3 hours to complete

Linear Classifiers & Logistic Regression

3 hours to complete
18 videos (Total 78 min), 2 readings, 2 quizzes
18 videos
Intuition behind linear classifiers3m
Decision boundaries3m
Linear classifier model5m
Effect of coefficient values on decision boundary2m
Using features of the inputs2m
Predicting class probabilities1m
Review of basics of probabilities6m
Review of basics of conditional probabilities8m
Using probabilities in classification2m
Predicting class probabilities with (generalized) linear models5m
The sigmoid (or logistic) link function4m
Logistic regression model5m
Effect of coefficient values on predicted probabilities7m
Overview of learning logistic regression models2m
Encoding categorical inputs4m
Multiclass classification with 1 versus all7m
Recap of logistic regression classifier1m
2 readings
Slides presented in this module10m
Predicting sentiment from product reviews10m
2 practice exercises
Linear Classifiers & Logistic Regression30m
Predicting sentiment from product reviews30m
Week
2

Week 2

3 hours to complete

Learning Linear Classifiers

3 hours to complete
18 videos (Total 83 min), 2 readings, 2 quizzes
18 videos
Intuition behind maximum likelihood estimation4m
Data likelihood8m
Finding best linear classifier with gradient ascent3m
Review of gradient ascent6m
Learning algorithm for logistic regression3m
Example of computing derivative for logistic regression5m
Interpreting derivative for logistic regression5m
Summary of gradient ascent for logistic regression2m
Choosing step size5m
Careful with step sizes that are too large4m
Rule of thumb for choosing step size3m
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick4m
(VERY OPTIONAL) Expressing the log-likelihood3m
(VERY OPTIONAL) Deriving probability y=-1 given x2m
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form8m
(VERY OPTIONAL) Deriving gradient of log likelihood8m
Recap of learning logistic regression classifiers1m
2 readings
Slides presented in this module10m
Implementing logistic regression from scratch10m
2 practice exercises
Learning Linear Classifiers30m
Implementing logistic regression from scratch30m
2 hours to complete

Overfitting & Regularization in Logistic Regression

2 hours to complete
13 videos (Total 66 min), 2 readings, 2 quizzes
13 videos
Review of overfitting in regression3m
Overfitting in classification5m
Visualizing overfitting with high-degree polynomial features3m
Overfitting in classifiers leads to overconfident predictions5m
Visualizing overconfident predictions4m
(OPTIONAL) Another perspecting on overfitting in logistic regression8m
Penalizing large coefficients to mitigate overfitting5m
L2 regularized logistic regression4m
Visualizing effect of L2 regularization in logistic regression5m
Learning L2 regularized logistic regression with gradient ascent7m
Sparse logistic regression with L1 regularization7m
Recap of overfitting & regularization in logistic regression58s
2 readings
Slides presented in this module10m
Logistic Regression with L2 regularization10m
2 practice exercises
Overfitting & Regularization in Logistic Regression30m
Logistic Regression with L2 regularization30m
Week
3

Week 3

3 hours to complete

Decision Trees

3 hours to complete
13 videos (Total 47 min), 3 readings, 3 quizzes
13 videos
Intuition behind decision trees1m
Task of learning decision trees from data3m
Recursive greedy algorithm4m
Learning a decision stump3m
Selecting best feature to split on6m
When to stop recursing4m
Making predictions with decision trees1m
Multiclass classification with decision trees2m
Threshold splits for continuous inputs6m
(OPTIONAL) Picking the best threshold to split on3m
Visualizing decision boundaries5m
Recap of decision trees56s
3 readings
Slides presented in this module10m
Identifying safe loans with decision trees10m
Implementing binary decision trees10m
3 practice exercises
Decision Trees30m
Identifying safe loans with decision trees14m
Implementing binary decision trees30m
Week
4

Week 4

2 hours to complete

Preventing Overfitting in Decision Trees

2 hours to complete
8 videos (Total 40 min), 2 readings, 2 quizzes
8 videos
Overfitting in decision trees5m
Principle of Occam's razor: Learning simpler decision trees5m
Early stopping in learning decision trees6m
(OPTIONAL) Motivating pruning8m
(OPTIONAL) Pruning decision trees to avoid overfitting6m
(OPTIONAL) Tree pruning algorithm3m
Recap of overfitting and regularization in decision trees1m
2 readings
Slides presented in this module10m
Decision Trees in Practice10m
2 practice exercises
Preventing Overfitting in Decision Trees30m
Decision Trees in Practice30m
1 hour to complete

Handling Missing Data

1 hour to complete
6 videos (Total 25 min), 1 reading, 1 quiz
6 videos
Strategy 1: Purification by skipping missing data4m
Strategy 2: Purification by imputing missing data4m
Modifying decision trees to handle missing data4m
Feature split selection with missing data5m
Recap of handling missing data1m
1 reading
Slides presented in this module10m
1 practice exercise
Handling Missing Data30m

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