ML basics

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

The four areas of machine learning education

When beginning your educational path, it's important to first understand how to learn ML. We've broken the learning process into four areas of knowledge, with each area providing a foundational piece of the ML puzzle. To help you on your path, we've identified books, videos, and online courses that will uplevel your abilities, and prepare you to use ML for your projects. Start with our guided curriculums designed to increase your knowledge, or choose your own path by exploring our resource library.

• Coding skills: Building ML models involves much more than just knowing ML concepts—it requires coding in order to do the data management, parameter tuning, and parsing results needed to test and optimize your model.

• Math and stats: ML is a math heavy discipline, so if you plan to modify ML models or build new ones from scratch, familiarity with the underlying math concepts is crucial to the process.

• ML theory: Knowing the basics of ML theory will give you a foundation to build on, and help you troubleshoot when something goes wrong.

• Build your own projects: Getting hands on experience with ML is the best way to put your knowledge to the test, so don't be afraid to dive in early with a simple colab or tutorial to get some practice.