Cursos de Data Mining

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.  After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:IBM Learning for Data and AI Individual Subscription (SUBR022G)IBM Learning for Data and AI Enterprise Subscription (SUBR004G)IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning as well as additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. You will learn how to find and analyze data with a time component and censored data that needs outcome inference.  You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices for unsupervised learning and verifying assumptions derived from Statistical learning. IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:IBM Learning for Data and AI Individual Subscription (SUBR022G)IBM Learning for Data and AI Enterprise Subscription (SUBR004G)IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)

This course introduces you to two of the main types of modelling families of supervised Machine Learning: Regression and Classification. You start by learning how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. You then learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:IBM Learning for Data and AI Individual Subscription (SUBR022G)IBM Learning for Data and AI Enterprise Subscription (SUBR004G)IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)