Machine learning has become more accessible over the years and several tools and platforms have popped up to make it easier to learn and implement the basics. For example, in minutes, you can use Google's Teachable Machine to create data, train the data and have a model that classifies, with varying confidence, what it sees. The video below is a brief example of this.
This model classifies whether I am drinking water or not.
These types of no-code or low code tools can be a great way to learn a little about machine learning and familiarize yourself with concepts like training data, models and expected outcomes. That being said, you will still need to code to implement this model in your own application.
Python has grown in popularity over the years and maintained its title as the leading programming language for machine learning. In 2017, 57% of data scientists and machine learning developers used Python. This number has grown in more recent studies to 67% of machine learning developers and data scientists using Python. Python is a language that is relatively easy to learn and use, it also comes with a wide array of libraries to turbocharge your machine learning projects. Some useful resources for getting started with Machine Learning in Python include Machine Learning with Python.
When it comes to popular programming languages for machine learning, R is also a good one to know. R is a language that was created by and for statisticians. R features prominently in projects that rely heavily on statistics, but its limited versatility outside of statistics, and its steep learning curve, make it inferior to Python in industry.
C, C++ and Java are also languages often used for machine learning. C/C++ are low level programming languages with a steep learning curve but those who have mastered this language are well positioned to improve existing implementations of machine learning algorithms. Java, the third most popular language among experienced professional developers, is a common choice for those with a background in Java development. Christina Voskoglou, Senior Director of Research at SlashData, writes that, “Java is gaining popularity among machine learning engineers who hail from a Java development background as they don’t need to learn a new programming language like Python or R to implement machine learning.” If you are interested in learning more about machine learning and you have a background in Java, there are several libraries in Java for machine learning, including Apache Mahout, Deeplearning4j and Mallet, but the most widely recommended is Weka.