À propos de ce cours : 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.
Les destinataires de ce cours : This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Créé par : University of Michigan
Enseigné par : Kevyn Collins-Thompson, Associate Professor
School of Information
| Informations de base | Cours 3 de 5 dans Applied Data Science with Python Specialization |
| Niveau | Intermédiaire |
| Langue | English, Sous-titres : Korean |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
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.
6 vidéos, 4 lectures
- 阅读: Course Syllabus
- 阅读: Help us learn more about you!
- Vidéo: Key Concepts in Machine Learning
- Vidéo: Python Tools for Machine Learning
- 阅读: Notice for Auditing Learners: Assignment Submission
- 笔记本: Module 1 Notebook
- Vidéo: An Example Machine Learning Problem
- Vidéo: Examining the Data
- Vidéo: K-Nearest Neighbors Classification
- 阅读: Zachary Lipton: The Foundations of Algorithmic Bias (optional)
- 笔记本: Assignment 1
Noté: Module 1 Quiz
Noté: Assignment 1 Submission
SEMAINE 2
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.
12 vidéos, 2 lectures
- 笔记本: Module 2 Notebook
- Vidéo: Overfitting and Underfitting
- Vidéo: Supervised Learning: Datasets
- Vidéo: K-Nearest Neighbors: Classification and Regression
- Vidéo: Linear Regression: Least-Squares
- Vidéo: Linear Regression: Ridge, Lasso, and Polynomial Regression
- Vidéo: Logistic Regression
- Vidéo: Linear Classifiers: Support Vector Machines
- Vidéo: Multi-Class Classification
- Vidéo: Kernelized Support Vector Machines
- Vidéo: Cross-Validation
- Vidéo: Decision Trees
- 阅读: A Few Useful Things to Know about Machine Learning
- 阅读: Ed Yong: Genetic Test for Autism Refuted (optional)
- 笔记本: Classifier Visualization Playspace
- 笔记本: Assignment 2
Noté: Module 2 Quiz
Noté: Assignment 2 Submission
SEMAINE 3
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.
7 vidéos, 1 lecture
- 笔记本: Module 3 Notebook
- Vidéo: Confusion Matrices & Basic Evaluation Metrics
- Vidéo: Classifier Decision Functions
- Vidéo: Precision-recall and ROC curves
- Vidéo: Multi-Class Evaluation
- Vidéo: Regression Evaluation
- 阅读: Practical Guide to Controlled Experiments on the Web (optional)
- Vidéo: Model Selection: Optimizing Classifiers for Different Evaluation Metrics
- 笔记本: Assignment 3
Noté: Module 3 Quiz
Noté: Assignment 3 Submission
SEMAINE 4
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.
10 vidéos, 11 lectures
- 笔记本: Module 4 Notebook
- Vidéo: Random Forests
- Vidéo: Gradient Boosted Decision Trees
- Vidéo: Neural Networks
- 阅读: Neural Networks Made Easy (optional)
- 阅读: Play with Neural Networks: TensorFlow Playground (optional)
- Vidéo: Deep Learning (Optional)
- 阅读: Deep Learning in a Nutshell: Core Concepts (optional)
- 阅读: Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
- Vidéo: Data Leakage
- 阅读: The Treachery of Leakage (optional)
- 阅读: Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
- 阅读: Data Leakage Example: The ICML 2013 Whale Challenge (optional)
- 阅读: Rules of Machine Learning: Best Practices for ML Engineering (optional)
- 笔记本: Assignment 4
- 笔记本: Unsupervised Learning Notebook
- Vidéo: Introduction
- Vidéo: Dimensionality Reduction and Manifold Learning
- Vidéo: Clustering
- 阅读: How to Use t-SNE Effectively
- 阅读: How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
- Vidéo: Conclusion
- 阅读: Post-course Survey
Noté: Module 4 Quiz
Noté: Assignment 4 Submission
FAQ
Comment cela fonctionne
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Good course!!
Good applied material to study along theoretical material!
there are some gaps which is really difficult to understand！
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HR
Great course!!