About this Course
4.6
2,496 ratings
470 reviews
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 24 hours to complete

Suggested: 8 hours/week...
Available languages

English

Subtitles: English, Korean...

What you will learn

  • Check

    Build features that meet analysis needs

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    Create and evaluate data clusters

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    Describe how machine learning is different than descriptive statistics

  • Check

    Explain different approaches for creating predictive models

Skills you will gain

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 24 hours to complete

Suggested: 8 hours/week...
Available languages

English

Subtitles: English, Korean...

Syllabus - What you will learn from this course

Week
1
Hours to complete
8 hours to complete

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

This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library....
Reading
6 videos (Total 71 min), 4 readings, 2 quizzes
Video6 videos
Key Concepts in Machine Learning13m
Python Tools for Machine Learning4m
An Example Machine Learning Problem12m
Examining the Data9m
K-Nearest Neighbors Classification20m
Reading4 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
Quiz1 practice exercise
Module 1 Quiz20m
Week
2
Hours to complete
9 hours to complete

Module 2: Supervised Machine Learning - Part 1

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees. ...
Reading
12 videos (Total 166 min), 2 readings, 2 quizzes
Video12 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
Reading2 readings
A Few Useful Things to Know about Machine Learning10m
Ed Yong: Genetic Test for Autism Refuted (optional)10m
Quiz1 practice exercise
Module 2 Quiz22m
Week
3
Hours to complete
7 hours to complete

Module 3: Evaluation

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models. ...
Reading
7 videos (Total 81 min), 1 reading, 2 quizzes
Video7 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
Reading1 reading
Practical Guide to Controlled Experiments on the Web (optional)10m
Quiz1 practice exercise
Module 3 Quiz28m
Week
4
Hours to complete
10 hours to complete

Module 4: Supervised Machine Learning - Part 2

This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it....
Reading
10 videos (Total 94 min), 11 readings, 2 quizzes
Video10 videos
Random Forests11m
Gradient Boosted Decision Trees5m
Neural Networks19m
Deep Learning (Optional)7m
Data Leakage11m
Introduction4m
Dimensionality Reduction and Manifold Learning9m
Clustering14m
Conclusion2m
Reading11 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
Quiz1 practice exercise
Module 4 Quiz20m
4.6
Career direction

55%

started a new career after completing these courses
Career Benefit

83%

got a tangible career benefit from this course

Top Reviews

By FLOct 14th 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

By OASep 9th 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

Instructor

Avatar

Kevyn Collins-Thompson

Associate Professor
School of Information

About University of Michigan

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future....

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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