About this Course

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Learner Career Outcomes

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started a new career after completing these courses

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got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
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Approx. 7 hours to complete
English

Skills you will gain

Random ForestPredictive AnalyticsMachine LearningR Programming

Learner Career Outcomes

25%

started a new career after completing these courses

20%

got a tangible career benefit from this course
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. 7 hours to complete
English

Offered by

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University of Washington

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

Practical Statistical Inference

2 hours to complete
28 videos (Total 121 min)
28 videos
Hypothesis Testing5m
Significance Tests and P-Values3m
Example: Difference of Means4m
Deriving the Sampling Distribution6m
Shuffle Test for Significance4m
Comparing Classical and Resampling Methods3m
Bootstrap6m
Resampling Caveats6m
Outliers and Rank Transformation3m
Example: Chi-Squared Test3m
Bad Science Revisited: Publication Bias4m
Effect Size4m
Meta-analysis5m
Fraud and Benford's Law4m
Intuition for Benford's Law2m
Benford's Law Explained Visually3m
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3m
Multiple Hypothesis Testing: False Discovery Rate4m
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3m
Big Data and Spurious Correlations4m
Spurious Correlations: Stock Price Example3m
How is Big Data Different?3m
Bayesian vs. Frequentist4m
Motivation for Bayesian Approaches3m
Bayes' Theorem2m
Applying Bayes' Theorem4m
Naive Bayes: Spam Filtering4m
Week
2

Week 2

3 hours to complete

Supervised Learning

3 hours to complete
26 videos (Total 111 min), 1 reading, 1 quiz
26 videos
Simple Examples3m
Structure of a Machine Learning Problem5m
Classification with Simple Rules5m
Learning Rules4m
Rules: Sequential Covering3m
Rules Recap2m
From Rules to Trees2m
Entropy4m
Measuring Entropy4m
Using Information Gain to Build Trees6m
Building Trees: ID3 Algorithm2m
Building Trees: C.45 Algorithm4m
Rules and Trees Recap3m
Overfitting7m
Evaluation: Leave One Out Cross Validation5m
Evaluation: Accuracy and ROC Curves5m
Bootstrap Revisited4m
Ensembles, Bagging, Boosting4m
Boosting Walkthrough5m
Random Forests3m
Random Forests: Variable Importance5m
Summary: Trees and Forests2m
Nearest Neighbor4m
Nearest Neighbor: Similarity Functions4m
Nearest Neighbor: Curse of Dimensionality3m
1 reading
R Assignment: Classification of Ocean Microbes10m
1 practice exercise
R Assignment: Classification of Ocean Microbes30m
Week
3

Week 3

1 hour to complete

Optimization

1 hour to complete
11 videos (Total 41 min)
11 videos
Gradient Descent Visually4m
Gradient Descent in Detail2m
Gradient Descent: Questions to Consider3m
Intuition for Logistic Regression4m
Intuition for Support Vector Machines3m
Support Vector Machine Example3m
Intuition for Regularization3m
Intuition for LASSO and Ridge Regression3m
Stochastic and Batched Gradient Descent5m
Parallelizing Gradient Descent3m
Week
4

Week 4

1 hour to complete

Unsupervised Learning

1 hour to complete
4 videos (Total 21 min)
4 videos
K-means5m
DBSCAN4m
DBSCAN Variable Density and Parallel Algorithms4m

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About the Data Science at Scale Specialization

Data Science at Scale

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