课程信息
4.6
2,371 个评分
454 个审阅
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....
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

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Intermediate Level

中级

Clock

Approx. 23 hours to complete

建议:8 hours/week...
Comment Dots

English

字幕:English, Korean...

您将学到的内容有

  • Check
    Build features that meet analysis needs
  • Check
    Create and evaluate data clusters
  • Check
    Describe how machine learning is different than descriptive statistics
  • Check
    Explain different approaches for creating predictive models

您将获得的技能

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Intermediate Level

中级

Clock

Approx. 23 hours to complete

建议:8 hours/week...
Comment Dots

English

字幕:English, Korean...

教学大纲 - 您将从这门课程中学到什么

Week
1
Clock
完成时间为 8 小时

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 个视频(共 71 分钟), 4 个阅读材料, 2 个测验
Video6 个视频
Introduction11分钟
Key Concepts in Machine Learning13分钟
Python Tools for Machine Learning4分钟
An Example Machine Learning Problem12分钟
Examining the Data9分钟
K-Nearest Neighbors Classification20分钟
Reading4 个阅读材料
Course Syllabus10分钟
Help us learn more about you!10分钟
Notice for Auditing Learners: Assignment Submission10分钟
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30分钟
Quiz1 个练习
Module 1 Quiz20分钟
Week
2
Clock
完成时间为 9 小时

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 个视频(共 166 分钟), 2 个阅读材料, 2 个测验
Video12 个视频
Overfitting and Underfitting12分钟
Supervised Learning: Datasets4分钟
K-Nearest Neighbors: Classification and Regression13分钟
Linear Regression: Least-Squares17分钟
Linear Regression: Ridge, Lasso, and Polynomial Regression19分钟
Logistic Regression12分钟
Linear Classifiers: Support Vector Machines13分钟
Multi-Class Classification6分钟
Kernelized Support Vector Machines18分钟
Cross-Validation9分钟
Decision Trees19分钟
Reading2 个阅读材料
A Few Useful Things to Know about Machine Learning10分钟
Ed Yong: Genetic Test for Autism Refuted (optional)10分钟
Quiz1 个练习
Module 2 Quiz22分钟
Week
3
Clock
完成时间为 7 小时

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 个视频(共 81 分钟), 1 个阅读材料, 2 个测验
Video7 个视频
Confusion Matrices & Basic Evaluation Metrics12分钟
Classifier Decision Functions7分钟
Precision-recall and ROC curves6分钟
Multi-Class Evaluation13分钟
Regression Evaluation6分钟
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13分钟
Reading1 个阅读材料
Practical Guide to Controlled Experiments on the Web (optional)10分钟
Quiz1 个练习
Module 3 Quiz28分钟
Week
4
Clock
完成时间为 10 小时

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 个视频(共 94 分钟), 11 个阅读材料, 2 个测验
Video10 个视频
Random Forests11分钟
Gradient Boosted Decision Trees5分钟
Neural Networks19分钟
Deep Learning (Optional)7分钟
Data Leakage11分钟
Introduction4分钟
Dimensionality Reduction and Manifold Learning9分钟
Clustering14分钟
Conclusion2分钟
Reading11 个阅读材料
Neural Networks Made Easy (optional)10分钟
Play with Neural Networks: TensorFlow Playground (optional)10分钟
Deep Learning in a Nutshell: Core Concepts (optional)10分钟
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10分钟
The Treachery of Leakage (optional)10分钟
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10分钟
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10分钟
Rules of Machine Learning: Best Practices for ML Engineering (optional)10分钟
How to Use t-SNE Effectively10分钟
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10分钟
Post-course Survey10分钟
Quiz1 个练习
Module 4 Quiz20分钟
4.6
Direction Signs

55%

完成这些课程后已开始新的职业生涯
Briefcase

83%

通过此课程获得实实在在的工作福利

热门审阅

创建者 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!!

创建者 SSAug 19th 2017

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action

讲师

Kevyn Collins-Thompson

Associate Professor
School of Information

关于 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....

关于 Applied Data Science with Python 专项课程

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

常见问题

  • 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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