Acerca de este Curso

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Certificado para compartir
Obtén un certificado al finalizar
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. 6 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (de Brasil), Ruso (Russian), Inglés (English), Español (Spanish)
Certificado para compartir
Obtén un certificado al finalizar
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. 6 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (de Brasil), Ruso (Russian), Inglés (English), Español (Spanish)

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Alberta Machine Intelligence Institute

Programa - Qué aprenderás en este curso

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Semana
1

Semana 1

3 horas para completar

Introduction to Machine Learning Applications

3 horas para completar
12 videos (Total 44 minutos), 6 lecturas, 2 cuestionarios
12 videos
Instructor Introduction1m
Introduction to Course 12m
What is Artificial Intelligence and Machine Learning?5m
What about Data Science?3m
The Machine Learning Process4m
The Three Kinds of Machine Learning3m
Classification: What is it and how does it work?3m
Regression: Fitting lines and predicting numbers3m
Unsupervised Learning4m
Reinforcement Learning6m
Weekly Summary1m
6 lecturas
What about Deep Learning? (supplemental)10m
Fooling Neural Networks (supplemental)10m
How to Curate A Ground Truth For Your Business Dataset (Required)10m
Learning From Multiple Annotators: A Survey (supplemental)10m
Inferring the Ground Truth Through Crowdsourcing (supplemental)10m
Semi Supervised Learning (required)10m
2 ejercicios de práctica
Concepts and Definitions20m
Identifying Machine Learning Techniques10m
Semana
2

Semana 2

1 hora para completar

Machine Learning in the Real World

1 hora para completar
8 videos (Total 34 minutos), 4 lecturas, 1 cuestionario
8 videos
Features and transformations of raw data6m
Farmer Betty and Her Precision Agriculture Plans3m
What to consider when using your QuAM2m
Broad Examples Narrowed Down4m
Identify Business Evaluation4m
Everything is a Proxy4m
Weekly Summary2m
4 lecturas
A Brief Introduction into Precision Agriculture10m
Farmer Betty Tried Unsupervised Learning (required)10m
Data is Central to Your ML Problem (required)10m
Martin Zinkevich's Rules for ML (supplemental)10m
1 ejercicio de práctica
Machine Learning in the Real World Review
Semana
3

Semana 3

1 hora para completar

Learning Data

1 hora para completar
9 videos (Total 34 minutos), 2 lecturas, 1 cuestionario
9 videos
How Much Data Do I Need?4m
Ethical Issues4m
Bias in Data Sources3m
Noise and Sources of Randomness5m
Image Classification Example3m
Data Cleaning: Everybody's favourite task4m
Why you need to set up a Data Pipeline4m
Weekly Summary1m
2 lecturas
Data Protection Laws (required)10m
Government readings on data privacy (supplemental)10m
1 ejercicio de práctica
Understanding Data for ML
Semana
4

Semana 4

1 hora para completar

Machine Learning Projects

1 hora para completar
7 videos (Total 35 minutos), 2 lecturas, 1 cuestionario
7 videos
MLPL as experienced by Farmer Betty3m
Exploring the process of problem definition7m
Assessing your QuAM for use in your Business6m
Technically Assessing the Strength of your QuAM6m
Different Kinds of Wrong4m
Weekly Summary2m
2 lecturas
Machine Learning Process Lifecycle Explained10m
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10m
1 ejercicio de práctica
Understanding Machine Learning Projects

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Acerca de Programa especializado: Machine Learning: Algorithms in the Real World

This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning....
Machine Learning: Algorithms in the Real World

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