Este curso forma parte de Programa especializado: Machine Learning: Algorithms in the Real World

4.7
estrellas
405 calificaciones

Anna Koop

15.199 ya inscrito

ofrecido por

Programa especializado: Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence Institute

Acerca de este Curso

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This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).

This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

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Curso 2 de 4 en

Programa especializado: Machine Learning: Algorithms in the Real World

Aprox. 9 horas para completar

Inglés (English)

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 4 en

Programa especializado: Machine Learning: Algorithms in the Real World

Aprox. 9 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,7/5 (74 calificaciones)

Anna Koop

Senior Scientific Advisor

Alberta Machine Intelligence Institute, University of Alberta

32.110 alumnos

4 cursos

ofrecido por

Alberta Machine Intelligence Institute

Programa - Qué aprenderás en este curso

Semana

Semana 1

4 horas para completar

Classification using Decision Trees and k-NN

Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.

4 horas para completar

8 videos (Total 46 minutos), 4 lecturas, 2 cuestionarios

8 videos

Introduction to the Course1m

What does a classifier actually do?5m

Classification in scikit-learn3m

What are decision trees?6m

Generalization and overfitting8m

Classification using k-nearest neighbours8m

Distance measures8m

Weekly summary2m

4 lecturas

Math Review10m

Scikitlearn documentation for decision trees (Optional)10m

Scikitlearn documentation for random forests (Optional)10m

Scikitlearn documentation for k-nearest neighbours (Optional)10m

2 ejercicios de práctica

Supervised Learning Basics

Understanding Classification with Decision Trees and k-NN20m

Semana

Semana 2

2 horas para completar

Functions for Fun and Profit

Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.

2 horas para completar

9 videos (Total 62 minutos), 1 lectura, 4 cuestionarios

9 videos

Line-fitting6m

Optimal line-fitting8m

Loss and Convexity7m

Gradient Descent9m

Nonlinear features and model complexity6m

Bias and variance tradeoff6m

Regularizers5m

Loss for Classification7m

Weekly summary4m

1 lectura

Scikitlearn documentation for linear regression (Optional)10m

4 ejercicios de práctica

Regression Basics

Understanding Model Complexity

From Regression to Classification2m

The Regression side of Supervised Learning20m

Semana

Semana 3

3 horas para completar

Regression for Classification: Support Vector Machines

This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.

3 horas para completar

6 videos (Total 34 minutos), 1 lectura, 2 cuestionarios

Semana

Semana 4

1 hora para completar

Contrasting Models

Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.

1 hora para completar

8 videos (Total 46 minutos), 1 lectura, 1 cuestionario

Reseñas

4.7

66 reseña

5 stars
76,04 %
4 stars
18,51 %
3 stars
3,20 %
2 stars
0,98 %
1 star
1,23 %

Principales reseñas sobre MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL

por HM1 de may. de 2020

A good refresher on some commonly found learning algorithms.

por DS6 de may. de 2020

Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.

por SK11 de abr. de 2020

Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.

por CW29 de sep. de 2020

Great course, easy to grasp the main idea of how to assess and tune the performance of question-answering machines learned by machine learning algorithms through data

Ver todas las reseñas

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.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Machine Learning Algorithms: Supervised Learning Tip to Tail

Acerca de este Curso