This course is part of the Applied Data Science with Python Specialization

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Kevyn Collins-Thompson

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Applied Data Science with Python SpecializationUniversity of Michigan

About this Course

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This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. 
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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Course 3 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 31 hours to complete

English

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 Programming
Machine Learning (ML) Algorithms
Machine Learning
Scikit-Learn

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 31 hours to complete

English

Instructor

Instructor rating

4.45/5 (824 Ratings)

Kevyn Collins-Thompson

Associate Professor

School of Information

275,166 Learners

3 Courses

Offered by

University of Michigan

Start working towards your Master's degree

This course is related to the 100% online Master of Applied Data Science from University of Michigan. It will provide you with a preview of the topics, materials and instructors so you can decide if the full online degree program is right for you.

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Syllabus - What you will learn from this course

Content Rating92%(19,057 ratings)

Week1

Week 1

7 hours to complete

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

7 hours to complete

7 videos (Total 75 min), 4 readings, 2 quizzes

7 videos

Introduction11mWhat's New?58sKey Concepts in Machine Learning13mPython Tools for Machine Learning4mAn Example Machine Learning Problem12mExamining the Data9mK-Nearest Neighbors Classification23m

4 readings

Syllabus10mHelp us learn more about you!10mNotice for Auditing Learners: Assignment Submission10mZachary Lipton: The Foundations of Algorithmic Bias (optional)30m

1 practice exercise

Module 1 Quiz20m

Week2

Week 2

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.

9 hours to complete

13 videos (Total 190 min), 2 readings, 2 quizzes

13 videos

Introduction to Supervised Machine Learning17mOverfitting and Underfitting12mSupervised Learning: Datasets4mK-Nearest Neighbors: Classification and Regression13mLinear Regression: Least-Squares17mLinear Regression: Ridge, Lasso, and Polynomial Regression26mLogistic Regression12mLinear Classifiers: Support Vector Machines13mMulti-Class Classification6mKernelized Support Vector Machines18mCross-Validation12mDecision Trees19mOne-Hot Encoding (Optional)13m

2 readings

A Few Useful Things to Know about Machine Learning10mEd Yong: Genetic Test for Autism Refuted (optional)10m

1 practice exercise

Module 2 Quiz30m

Week3

Week 3

7 hours to complete

Module 3: Evaluation

7 hours to complete

8 videos (Total 113 min), 2 readings, 2 quizzes

8 videos

Model Evaluation & Selection22mConfusion Matrices & Basic Evaluation Metrics14mClassifier Decision Functions7mPrecision-Recall and ROC Curves7mMulti-Class Evaluation10mRegression Evaluation6mModel Selection: Optimizing Classifiers for Different Evaluation Metrics13mModel Calibration (Optional)31m

2 readings

Practical Guide to Controlled Experiments on the Web (optional)10mNote on Assignment 310m

1 practice exercise

Module 3 Quiz28m

Week4

Week 4

9 hours to complete

Module 4: Supervised Machine Learning - Part 2

9 hours to complete

10 videos (Total 103 min), 12 readings, 2 quizzes

10 videos

Naive Bayes Classifiers8mRandom Forests11mGradient Boosted Decision Trees5mNeural Networks18mDeep Learning (Optional)14mData Leakage13mIntroduction4mDimensionality Reduction and Manifold Learning9mClustering14mConclusion2m

12 readings

Neural Networks Made Easy (optional)10mPlay with Neural Networks: TensorFlow Playground (optional)10mDeep Learning in a Nutshell: Core Concepts (optional)10mAssisting Pathologists in Detecting Cancer with Deep Learning (optional)10mThe Treachery of Leakage (optional)10mLeakage in Data Mining: Formulation, Detection, and Avoidance (optional)10mData Leakage Example: The ICML 2013 Whale Challenge (optional)10mRules of Machine Learning: Best Practices for ML Engineering (optional)10mHow to Use t-SNE Effectively10mHow Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10mPost-course Survey10mKeep Learning with Michigan Online10m

1 practice exercise

Module 4 Quiz20m

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TOP REVIEWS FROM APPLIED MACHINE LEARNING IN PYTHON

by JZMay 5, 2020

Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.

Globally, very good course - strongly recommanded

by BSSep 17, 2020

Great content and good instruction. Need to fix the files in the assignments though. It's hard to keep track in the forums and frustrating go back and forth to find out why it's not working.

by PJApr 12, 2020

It feels good to learn something new and highly skilled demand in Engineering. Thanks to Coursera and instructor for providing such a wonderful opportunity of learning through your platform.

by OASep 8, 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

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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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Applied Machine Learning in Python

About this Course

What you will learn

Skills you will gain