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.
Este curso forma parte de Programa Especializado - Machine Learning: Algorithms in the Real World
ofrecido por
Programa Especializado - Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence InstituteAcerca 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.
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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.
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Programa - Qué aprenderás en este curso
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
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
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
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
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
6 videos
Neural Networks9m
Hinge Loss6m
Basics of Support Vector Machines6m
Kernels6m
Weekly Summary1m
1 lectura
Scikitlearn documentation for SVMs (Optional)10m
2 ejercicios de práctica
Understanding Support Vector Machines
Regression-based Classification10m
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
8 videos
Classification assessment6m
Learning Curves6m
Testing your models7m
Cross validation5m
Parameter tuning and grid search5m
Model Parameters6m
Weekly Summary1m
1 lectura
Some resources on model assessment (Optional)10m
1 ejercicio de práctica
Contrasting Models
Reseñas
Principales reseñas sobre MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL
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 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 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
por FF16 de abr. de 2020
Great course but less in-depth knowledge about each of the hyper parameters and under the hood view of Algorithms.But excellent. Thanks!!!!!!
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?
¿Qué recibiré si me suscribo a este Programa especializado?
Is financial aid available?
¿Recibiré crédito universitario por completar el Curso?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
