Machine learning (ML) is rapidly changing the world, from diverse types of applications and research pursued in industry and academia. Machine learning is affecting every part of our daily lives. From voice assistants using NLP and machine learning to make appointments, check our calendar and play music, to programmatic advertisements — that are so accurate that they can predict what we will need before we even think of it.
More often than not, the complexity of the scientific field of machine learning can be overwhelming, making keeping up with “what is important” a very challenging task. However, to make sure that we provide a learning path to those who seek to learn machine learning, but are new to these concepts. In this article, we look at the most critical basic algorithms that hopefully make your machine learning journey less challenging.
Any suggestions or feedback is crucial to continue to improve. Please let us know in the comments if you have any.
Introduction to Machine Learning.
Major Machine Learning Algorithms.
Supervised vs. Unsupervised Learning.
Multivariable Linear Regression.