If you are thinking about getting started in machine learning, there’s a lot to learn. A lot of people get overwhelmed, and then they don’t even begin. But if you break it down into pieces, you will see that machine learning is not as complicated as it seems. And with the right preparation and hard work, you can make your dream of becoming a machine learning expert a reality.
Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine Learning focuses on the development of computer programs that can access data and use them to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
There are three different types of machine learning:
-Supervised Learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
-Unsupervised Learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
-Reinforcement Learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent).
So how do you get started? Here are the top five prerequisites for getting started in machine learning:
-A Passion For The Subject
Machine learning can be complex, so you need to be genuinely interested in it if you want to succeed at it. You need to have a healthy curiosity about how machine learning works and a desire to know more about it. If you don’t have that, then it will be difficult to sustain the effort required to learn.
-A proficiency in calculus
A lot of machine learning involves calculus concepts such as derivatives and optimization problems. Having a basic understanding of these concepts will help you understand how certain algorithms function behind the scenes (and also help you debug your code)
-A Basic Understanding Of Programming
You do not have to become a software engineer, but you should have at least some familiarity with coding concepts and syntax. If you have ever written code before, that’s great if not, then take some time to learn at least one programming language so that you can understand how coding works and what programmers do when they write code.
-A Basic Understanding Of Linear Algebra
Linear algebra is the study of vectors, matrices, and their generalizations. It’s essential for understanding and manipulating data with machine learning. That’s why we make it a prerequisite course in our Machine Learning Engineer Nanodegree program.
-An Understanding Of Probabilities and Statistics
A good foundation in probability and statistics will ensure that you can correctly interpret the results of your algorithms. Without this knowledge, you risk making incorrect business decisions based on faulty algorithms.
So, what are the takeaways here? Well, if you are serious about developing your machine learning skills, it is best to start putting together a strong foundation.