Acerca de este Curso
4.7
3,216 calificaciones
585 revisiones

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 24 horas para completar

Sugerido: 8 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Qué aprenderás

  • 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

Habilidades que obtendrás

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 24 horas para completar

Sugerido: 8 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
8 horas para completar

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 (Total 71 minutos), 4 readings, 2 quizzes
6 videos
Key Concepts in Machine Learning13m
Python Tools for Machine Learning4m
An Example Machine Learning Problem12m
Examining the Data9m
K-Nearest Neighbors Classification20m
4 lecturas
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30m
1 ejercicio de práctica
Module 1 Quiz20m
Semana
2
9 horas para completar

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 (Total 166 minutos), 2 readings, 2 quizzes
12 videos
Overfitting and Underfitting12m
Supervised Learning: Datasets4m
K-Nearest Neighbors: Classification and Regression13m
Linear Regression: Least-Squares17m
Linear Regression: Ridge, Lasso, and Polynomial Regression19m
Logistic Regression12m
Linear Classifiers: Support Vector Machines13m
Multi-Class Classification6m
Kernelized Support Vector Machines18m
Cross-Validation9m
Decision Trees19m
2 lecturas
A Few Useful Things to Know about Machine Learning10m
Ed Yong: Genetic Test for Autism Refuted (optional)10m
1 ejercicio de práctica
Module 2 Quiz22m
Semana
3
7 horas para completar

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 (Total 81 minutos), 1 reading, 2 quizzes
7 videos
Confusion Matrices & Basic Evaluation Metrics12m
Classifier Decision Functions7m
Precision-recall and ROC curves6m
Multi-Class Evaluation13m
Regression Evaluation6m
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13m
1 lectura
Practical Guide to Controlled Experiments on the Web (optional)10m
1 ejercicio de práctica
Module 3 Quiz28m
Semana
4
10 horas para completar

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 (Total 94 minutos), 11 readings, 2 quizzes
10 videos
Random Forests11m
Gradient Boosted Decision Trees5m
Neural Networks19m
Deep Learning (Optional)7m
Data Leakage11m
Introduction4m
Dimensionality Reduction and Manifold Learning9m
Clustering14m
Conclusion2m
11 lecturas
Neural Networks Made Easy (optional)10m
Play with Neural Networks: TensorFlow Playground (optional)10m
Deep Learning in a Nutshell: Core Concepts (optional)10m
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10m
The Treachery of Leakage (optional)10m
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10m
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10m
Rules of Machine Learning: Best Practices for ML Engineering (optional)10m
How to Use t-SNE Effectively10m
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10m
Post-course Survey10m
1 ejercicio de práctica
Module 4 Quiz20m
4.7
585 revisionesChevron Right

41%

comenzó una nueva carrera después de completar estos cursos

39%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones

por 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!!

por 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

Instructor

Avatar

Kevyn Collins-Thompson

Associate Professor
School of Information

Acerca de Universidad de Míchigan

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....

Acerca del programa especializado Ciencias de los Datos Aplicada con Python

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....
Ciencias de los Datos Aplicada con Python

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