In recent years, the interest and application of Machine Learning (ML) has experienced such an expansion that it has become a discipline very often employed in industrial and academic research. There are a lot of tools that create very powerful predictive models.
The term ML encompasses the set of algorithms that allow identifying patterns present in data and creating models with them that represent them. Once models have been generated, they can be used to predict behaviors about events or events that have not yet been observed or predict past events. It is important to remember that ML systems are only able to memorize patterns that are present in the data with which they are trained, therefore, they can only recognize what they have seen before. By using systems trained with past data to predict futures, it is assumed that, in the future, the behavior will be the same, which is not always the case.
In this article I share free resources in the form of courses to learn that will surely help you start working in this branch of computer science that will grow in the coming years a lot.
Courses at Google
Google is always at the forefront of technology and technological advancements. From the developers.google.com address you can consult abundant basic and advanced machine learning courses. These courses range from machine learning basics to more complex tools. It is advisable to follow the order that comes on the page to gradually learn.
The goal of these courses is to help those with a basic understanding of machine learning benefit from Google’s ML best practices. If you took a class on ML, or if you built or worked on a machine learning model, you have the background to start taking these courses.
ML at Microsoft Training Course
Microsoft staff working on Azure offer a 12-week curriculum and 26 lessons on machine learning. the course can be found here -> ML-For-Beginners. In this curriculum, you’ll learn about what’s sometimes referred to as classic machine learning, primarily using Scikit-learn as a library and avoiding deep learning, which is covered in another curriculum dubbed “AI for Beginners.”
The course offers a journey around the world while applying these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions for completing the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn as you build, a proven way for new skills to ‘stick’. There is also a very interesting Data Science course in this direction -> Data-Science-For-Beginners.
Amazon Web Services (AWS) course
Amazon Web Services offers a learning plan that brings together training content for a particular role or solution and orchestrates those assets from basic to advanced. There are several learning plans as a starting point to discover training that may be more interesting than another.
This learning plan is designed to help developers and data scientists integrate ML and artificial intelligence (AI) into tools and applications. The digital training included in this learning plan showcases a broader and deeper set of machine learning services and a cloud support infrastructure. This learning plan can also help you prepare for the AWS Certified ML – Specialty certification exam.
If you’re interested in accessing other resources, you can explore the Ramp-Up Guide: Machine Learning .
Course on Coursera
The Coursera platform also has a Specialization course where you will be able to do the following:
- Create machine learning models in Python using the popular machine learning libraries NumPy and scikit-learn.
- Create and train supervised ML models for binary prediction and classification tasks, including linear regression and logistic regression.
The ML Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner program, you’ll learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
Courses at Google Colelabs
I also share another course within the Google domain. Google Developers Codelabs provides guided tutorials that emphasize practical coding examples. Most tutorials will walk you through the process of creating a small app or adding a new feature to an existing app. They cover a wide range of topics such as Android Wear, Google Compute Engine, Project Tango, and Google’s APIs on iOS. To browse the Codelabs website, visit https://codelabs.developers.google.com/