Informações sobre o curso

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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 34 horas para completar
Inglês
Legendas: Inglês, Coreano

O que você vai aprender

  • Describe how machine learning is different than descriptive statistics

  • Create and evaluate data clusters

  • Explain different approaches for creating predictive models

  • Build features that meet analysis needs

Habilidades que você terá

Python ProgrammingMachine Learning (ML) AlgorithmsMachine LearningScikit-Learn

Resultados de carreira do aprendiz

34%

comecei uma nova carreira após concluir estes cursos

35%

consegui um benefício significativo de carreira com este curso

12%

recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 34 horas para completar
Inglês
Legendas: Inglês, Coreano

Instrutores

oferecido por

Logotipo de Universidade de Michigan

Universidade de Michigan

Programa - O que você aprenderá com este curso

Classificação do conteúdoThumbs Up91%(11,137 classificações)Info
Semana
1

Semana 1

8 horas para concluir

Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn

8 horas para concluir
6 vídeos (Total 71 mín.), 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

Semana 2

9 horas para concluir

Module 2: Supervised Machine Learning - Part 1

9 horas para concluir
12 vídeos (Total 166 mín.), 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

Semana 3

7 horas para concluir

Module 3: Evaluation

7 horas para concluir
7 vídeos (Total 81 mín.), 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

Semana 4

10 horas para concluir

Module 4: Supervised Machine Learning - Part 2

10 horas para concluir
10 vídeos (Total 94 mín.), 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

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Sobre 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

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