課程信息
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100% 在線

立即開始,按照自己的計劃學習。

第 3 門課程(共 5 門)

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為24 小時

建議:9 hours/week...

英語(English)

字幕:英語(English), 韓語

您將學到的內容有

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    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

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    Explain different approaches for creating predictive models

您將獲得的技能

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn

100% 在線

立即開始,按照自己的計劃學習。

第 3 門課程(共 5 門)

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為24 小時

建議:9 hours/week...

英語(English)

字幕:英語(English), 韓語

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 8 小時

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

6 個視頻 (總計 71 分鐘), 4 個閱讀材料, 2 個測驗
6 個視頻
Introduction11分鐘
Key Concepts in Machine Learning13分鐘
Python Tools for Machine Learning4分鐘
An Example Machine Learning Problem12分鐘
Examining the Data9分鐘
K-Nearest Neighbors Classification20分鐘
4 個閱讀材料
Course Syllabus10分鐘
Help us learn more about you!10分鐘
Notice for Auditing Learners: Assignment Submission10分鐘
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30分鐘
1 個練習
Module 1 Quiz20分鐘
2
完成時間為 9 小時

Module 2: Supervised Machine Learning - Part 1

12 個視頻 (總計 166 分鐘), 2 個閱讀材料, 2 個測驗
12 個視頻
Overfitting and Underfitting12分鐘
Supervised Learning: Datasets4分鐘
K-Nearest Neighbors: Classification and Regression13分鐘
Linear Regression: Least-Squares17分鐘
Linear Regression: Ridge, Lasso, and Polynomial Regression19分鐘
Logistic Regression12分鐘
Linear Classifiers: Support Vector Machines13分鐘
Multi-Class Classification6分鐘
Kernelized Support Vector Machines18分鐘
Cross-Validation9分鐘
Decision Trees19分鐘
2 個閱讀材料
A Few Useful Things to Know about Machine Learning10分鐘
Ed Yong: Genetic Test for Autism Refuted (optional)10分鐘
1 個練習
Module 2 Quiz22分鐘
3
完成時間為 7 小時

Module 3: Evaluation

7 個視頻 (總計 81 分鐘), 1 個閱讀材料, 2 個測驗
7 個視頻
Confusion Matrices & Basic Evaluation Metrics12分鐘
Classifier Decision Functions7分鐘
Precision-recall and ROC curves6分鐘
Multi-Class Evaluation13分鐘
Regression Evaluation6分鐘
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13分鐘
1 個閱讀材料
Practical Guide to Controlled Experiments on the Web (optional)10分鐘
1 個練習
Module 3 Quiz28分鐘
4
完成時間為 10 小時

Module 4: Supervised Machine Learning - Part 2

10 個視頻 (總計 94 分鐘), 11 個閱讀材料, 2 個測驗
10 個視頻
Random Forests11分鐘
Gradient Boosted Decision Trees5分鐘
Neural Networks19分鐘
Deep Learning (Optional)7分鐘
Data Leakage11分鐘
Introduction4分鐘
Dimensionality Reduction and Manifold Learning9分鐘
Clustering14分鐘
Conclusion2分鐘
11 個閱讀材料
Neural Networks Made Easy (optional)10分鐘
Play with Neural Networks: TensorFlow Playground (optional)10分鐘
Deep Learning in a Nutshell: Core Concepts (optional)10分鐘
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10分鐘
The Treachery of Leakage (optional)10分鐘
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10分鐘
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10分鐘
Rules of Machine Learning: Best Practices for ML Engineering (optional)10分鐘
How to Use t-SNE Effectively10分鐘
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10分鐘
Post-course Survey10分鐘
1 個練習
Module 4 Quiz20分鐘
4.6
757 條評論Chevron Right

34%

完成這些課程後已開始新的職業生涯

35%

通過此課程獲得實實在在的工作福利

12%

加薪或升職

來自Applied Machine Learning in Python的熱門評論

創建者 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

創建者 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!!

講師

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Kevyn Collins-Thompson

Associate Professor
School of Information

關於 密歇根大学

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....

關於 借助 Python 应用数据科学 專項課程

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....
借助 Python 应用数据科学

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