本课程是 Applied Data Science with Python 专项课程 专项课程的一部分

提供方

Applied Data Science with Python 专项课程

University of Michigan

课程信息

4.6

2,371 个评分

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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....

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

建议：8 hours/week...

字幕：English, Korean...

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

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

建议：8 hours/week...

字幕：English, Korean...

Week

1This 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....

6 个视频（共 71 分钟）, 4 个阅读材料, 2 个测验

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

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

Module 1 Quiz20分钟

Week

2This 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. ...

12 个视频（共 166 分钟）, 2 个阅读材料, 2 个测验

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

A Few Useful Things to Know about Machine Learning10分钟

Ed Yong: Genetic Test for Autism Refuted (optional)10分钟

Module 2 Quiz22分钟

Week

3This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models. ...

7 个视频（共 81 分钟）, 1 个阅读材料, 2 个测验

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

Practical Guide to Controlled Experiments on the Web (optional)10分钟

Module 3 Quiz28分钟

Week

4This 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....

10 个视频（共 94 分钟）, 11 个阅读材料, 2 个测验

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

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

Module 4 Quiz20分钟

4.6

完成这些课程后已开始新的职业生涯

通过此课程获得实实在在的工作福利

创建者 FL•Oct 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 SS•Aug 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

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....

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....

When will I have access to the lectures and assignments?

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.

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.

What is the refund policy?

Is financial aid available?

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