Informações sobre o curso
4.7
3,236 classificações
589 avaliações

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 24 horas para completar

Sugerido: 8 hours/week...

Inglês

Legendas: Inglês, Coreano

O que você vai aprender

  • 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 você terá

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 24 horas para completar

Sugerido: 8 hours/week...

Inglês

Legendas: Inglês, Coreano

Programa - O que você aprenderá com este curso

Semana
1
8 horas para concluir

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 vídeos (total de (Total 71 mín.) min), 4 leituras, 2 testes
6 videos
Key Concepts in Machine Learning13min
Python Tools for Machine Learning4min
An Example Machine Learning Problem12min
Examining the Data9min
K-Nearest Neighbors Classification20min
4 leituras
Course Syllabus10min
Help us learn more about you!10min
Notice for Auditing Learners: Assignment Submission10min
Zachary Lipton: The Foundations of Algorithmic Bias (optional)30min
1 exercício prático
Module 1 Quiz20min
Semana
2
9 horas para concluir

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 vídeos (total de (Total 166 mín.) min), 2 leituras, 2 testes
12 videos
Overfitting and Underfitting12min
Supervised Learning: Datasets4min
K-Nearest Neighbors: Classification and Regression13min
Linear Regression: Least-Squares17min
Linear Regression: Ridge, Lasso, and Polynomial Regression19min
Logistic Regression12min
Linear Classifiers: Support Vector Machines13min
Multi-Class Classification6min
Kernelized Support Vector Machines18min
Cross-Validation9min
Decision Trees19min
2 leituras
A Few Useful Things to Know about Machine Learning10min
Ed Yong: Genetic Test for Autism Refuted (optional)10min
1 exercício prático
Module 2 Quiz22min
Semana
3
7 horas para concluir

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 vídeos (total de (Total 81 mín.) min), 1 leitura, 2 testes
7 videos
Confusion Matrices & Basic Evaluation Metrics12min
Classifier Decision Functions7min
Precision-recall and ROC curves6min
Multi-Class Evaluation13min
Regression Evaluation6min
Model Selection: Optimizing Classifiers for Different Evaluation Metrics13min
1 leituras
Practical Guide to Controlled Experiments on the Web (optional)10min
1 exercício prático
Module 3 Quiz28min
Semana
4
10 horas para concluir

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 vídeos (total de (Total 94 mín.) min), 11 leituras, 2 testes
10 videos
Random Forests11min
Gradient Boosted Decision Trees5min
Neural Networks19min
Deep Learning (Optional)7min
Data Leakage11min
Introduction4min
Dimensionality Reduction and Manifold Learning9min
Clustering14min
Conclusion2min
11 leituras
Neural Networks Made Easy (optional)10min
Play with Neural Networks: TensorFlow Playground (optional)10min
Deep Learning in a Nutshell: Core Concepts (optional)10min
Assisting Pathologists in Detecting Cancer with Deep Learning (optional)10min
The Treachery of Leakage (optional)10min
Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)10min
Data Leakage Example: The ICML 2013 Whale Challenge (optional)10min
Rules of Machine Learning: Best Practices for ML Engineering (optional)10min
How to Use t-SNE Effectively10min
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms10min
Post-course Survey10min
1 exercício prático
Module 4 Quiz20min
4.7
589 avaliaçõesChevron Right

41%

comecei uma nova carreira após concluir estes cursos

39%

consegui um benefício significativo de carreira com este curso

Melhores avaliações

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

Instrutores

Avatar

Kevyn Collins-Thompson

Associate Professor
School of Information

Sobre Universidade de 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....

Sobre o Programa de cursos integrados Ciência de dados aplicada com 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....
Ciência de dados aplicada com Python

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.