About this Course

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Learner Career Outcomes

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started a new career after completing these courses

35%

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12%

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Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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Intermediate Level
Approx. 34 hours to complete
English
Subtitles: English, Korean

What you will learn

  • Describe how machine learning is different than descriptive statistics

  • Create and evaluate data clusters

  • Explain different approaches for creating predictive models

  • Build features that meet analysis needs

Skills you will gain

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn

Learner Career Outcomes

34%

started a new career after completing these courses

35%

got a tangible career benefit from this course

12%

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.
Intermediate Level
Approx. 34 hours to complete
English
Subtitles: English, Korean

Offered by

University of Michigan logo

University of Michigan

Syllabus - What you will learn from this course

Content RatingThumbs Up91%(11,941 ratings)Info
Week
1

Week 1

8 hours to complete

Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn

8 hours to complete
6 videos (Total 71 min), 4 readings, 2 quizzes
6 videos
Key Concepts in Machine Learning13m
Python Tools for Machine Learning4m
An Example Machine Learning Problem12m
Examining the Data9m
K-Nearest Neighbors Classification20m
4 readings
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30m
1 practice exercise
Module 1 Quiz20m
Week
2

Week 2

9 hours to complete

Module 2: Supervised Machine Learning - Part 1

9 hours to complete
12 videos (Total 166 min), 2 readings, 2 quizzes
12 videos
Overfitting and Underfitting12m
Supervised Learning: Datasets4m
K-Nearest Neighbors: Classification and Regression13m
Linear Regression: Least-Squares17m
Linear Regression: Ridge, Lasso, and Polynomial Regression19m
Logistic Regression12m
Linear Classifiers: Support Vector Machines13m
Multi-Class Classification6m
Kernelized Support Vector Machines18m
Cross-Validation9m
Decision Trees19m
2 readings
A Few Useful Things to Know about Machine Learning10m
Ed Yong: Genetic Test for Autism Refuted (optional)10m
1 practice exercise
Module 2 Quiz22m
Week
3

Week 3

7 hours to complete

Module 3: Evaluation

7 hours to complete
7 videos (Total 81 min), 1 reading, 2 quizzes
7 videos
Confusion Matrices & Basic Evaluation Metrics12m
Classifier Decision Functions7m
Precision-recall and ROC curves6m
Multi-Class Evaluation13m
Regression Evaluation6m
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13m
1 reading
Practical Guide to Controlled Experiments on the Web (optional)10m
1 practice exercise
Module 3 Quiz28m
Week
4

Week 4

10 hours to complete

Module 4: Supervised Machine Learning - Part 2

10 hours to complete
10 videos (Total 94 min), 11 readings, 2 quizzes
10 videos
Random Forests11m
Gradient Boosted Decision Trees5m
Neural Networks19m
Deep Learning (Optional)7m
Data Leakage11m
Introduction4m
Dimensionality Reduction and Manifold Learning9m
Clustering14m
Conclusion2m
11 readings
Neural Networks Made Easy (optional)10m
Play with Neural Networks: TensorFlow Playground (optional)10m
Deep Learning in a Nutshell: Core Concepts (optional)10m
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10m
The Treachery of Leakage (optional)10m
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10m
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10m
Rules of Machine Learning: Best Practices for ML Engineering (optional)10m
How to Use t-SNE Effectively10m
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10m
Post-course Survey10m
1 practice exercise
Module 4 Quiz20m

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About the Applied Data Science with Python Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate....
Applied Data Science with Python

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