| Sumario: | According to Forbes (Forbes, 2018), as of 2018, more than 3.7 billion users use the Internet and around 5 billion searches were executed on available search engines. By 2021 and according to the World Economic Forum (World Economic Forum, 2020), every day users produced more than 100 exabytes of data on social networks worldwide, which represents the complete content of around 200 million laptops with average storage capacity. Adding to those numbers the data generated by Internet of Things (IoT) devices, mobile devices, and use of cloud services, among many others (Popić, Velikic, Teslic, & Pavkovic, 2019), a considerably high magnitude of data is obtained that is stored and shared on a daily basis. Therefore, analyzing and generating knowledge from this data represents a competitive advantage in any business context, as well as a way to model the environment to discover new patterns. This course covers interpretable methods of supervised and unsupervised machine learning with examples for pattern modeling. The most-used techniques of regression, classification, clustering, and dimensionality reduction are presented, showing their implementation in Python. By studying the aforementioned content, you will have the skills to develop data science algorithms focused on Machine Learning, enabling you to practice them in your day-to-day work as an IT professional. If your goal is a role change to data-related roles, this course will provide you with enough tools to make your new data knowledge a foundation for a lasting career plan.
|
|---|
