Deep Learning Fundamentals: A Python-Based Course

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 Deep Learning methods with examples for pattern modeling in Python, including Artificial Neural Networks and Convolutional Neural Networks. By studying the aforementioned content, you will have the skills to develop intermediate data science algorithms considering key optimization methods such as Gradient Descent and its variations, backpropagation, convolution operations and model tuning, among others.

What you’ll learn and how to apply it

• Understand in-depth machine learning concepts such as regression and classification algorithms, clustering and dimensionality reduction, and deep learning techniques;
• apply Machine Learning algorithms in a practical setting solving supervised and/or unsupervised problems that require pattern recognition and modeling, and,
• develop algorithms in Python to implement Machine Learning and Deep Learning methods within the business environment.

This course is for you because

• You are a developer interested in learning the fundamentals of deep learning and its applications.
• You want to become more proficient at Machine Learning practices, developing deep learning algorithms and building Machine Learning pipelines to model the behavior of the variables at your company.
• Machine Learning is an adjacent technology that you would like to understand in depth.

Prerequisites

• This is a foundational and intermediate course. Anyone with the following set of skills can take the course:
  • Structure programming in Python
  • Object oriented programming in Python
  • Basic supervised and unsupervised learning knowledge