Acerca de este curso: 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.
Para quién es esta clase: 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.
Creada por: University of Michigan
Enseñado por: Kevyn Collins-Thompson, Associate Professor
School of Information
| Información básica | Curso 3 de 5 en Applied Data Science with Python Specialization |
| Nivel | Intermedio |
| Idioma | English, Subtítulos: Korean |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 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 videos, 4 lecturas
- Reading: Course Syllabus
- Reading: Help us learn more about you!
- Vídeo: Key Concepts in Machine Learning
- Vídeo: Python Tools for Machine Learning
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Module 1 Notebook
- Vídeo: An Example Machine Learning Problem
- Vídeo: Examining the Data
- Vídeo: K-Nearest Neighbors Classification
- Reading: Zachary Lipton: The Foundations of Algorithmic Bias (optional)
- Notebook: Assignment 1
Calificado: Module 1 Quiz
Calificado: Assignment 1 Submission
SEMANA 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 videos, 2 lecturas
- Notebook: Module 2 Notebook
- Vídeo: Overfitting and Underfitting
- Vídeo: Supervised Learning: Datasets
- Vídeo: K-Nearest Neighbors: Classification and Regression
- Vídeo: Linear Regression: Least-Squares
- Vídeo: Linear Regression: Ridge, Lasso, and Polynomial Regression
- Vídeo: Logistic Regression
- Vídeo: Linear Classifiers: Support Vector Machines
- Vídeo: Multi-Class Classification
- Vídeo: Kernelized Support Vector Machines
- Vídeo: Cross-Validation
- Vídeo: Decision Trees
- Reading: A Few Useful Things to Know about Machine Learning
- Reading: Ed Yong: Genetic Test for Autism Refuted (optional)
- Notebook: Classifier Visualization Playspace
- Notebook: Assignment 2
Calificado: Module 2 Quiz
Calificado: Assignment 2 Submission
SEMANA 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 videos, 1 lectura
- Notebook: Module 3 Notebook
- Vídeo: Confusion Matrices & Basic Evaluation Metrics
- Vídeo: Classifier Decision Functions
- Vídeo: Precision-recall and ROC curves
- Vídeo: Multi-Class Evaluation
- Vídeo: Regression Evaluation
- Reading: Practical Guide to Controlled Experiments on the Web (optional)
- Vídeo: Model Selection: Optimizing Classifiers for Different Evaluation Metrics
- Notebook: Assignment 3
Calificado: Module 3 Quiz
Calificado: Assignment 3 Submission
SEMANA 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 videos, 11 lecturas
- Notebook: Module 4 Notebook
- Vídeo: Random Forests
- Vídeo: Gradient Boosted Decision Trees
- Vídeo: Neural Networks
- Reading: Neural Networks Made Easy (optional)
- Reading: Play with Neural Networks: TensorFlow Playground (optional)
- Vídeo: Deep Learning (Optional)
- Reading: Deep Learning in a Nutshell: Core Concepts (optional)
- Reading: Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
- Vídeo: Data Leakage
- Reading: The Treachery of Leakage (optional)
- Reading: Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
- Reading: Data Leakage Example: The ICML 2013 Whale Challenge (optional)
- Reading: Rules of Machine Learning: Best Practices for ML Engineering (optional)
- Notebook: Assignment 4
- Notebook: Unsupervised Learning Notebook
- Vídeo: Introduction
- Vídeo: Dimensionality Reduction and Manifold Learning
- Vídeo: Clustering
- Reading: How to Use t-SNE Effectively
- Reading: How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
- Vídeo: Conclusion
- Reading: Post-course Survey
Calificado: Module 4 Quiz
Calificado: Assignment 4 Submission
Preguntas Frecuentes
Cómo funciona
Coursework
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Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
Excellent course.
Great review of ML
Nice there could just be a bit more of a case study to see the difference and decision ways in practices
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李
quite good， through this course I have gained knowledge and basic concept of machine learning and how to use python to run these machine learning models.