5 Skills You Need to Learn For Machine Learning

With any new expertise, skills, or professional path, you probably have a bigger number of inquiries than addresses. How would I begin? What abilities do I have to focus on first? What sources do I trust to gain proficiency with all of this? Information science and AI are the same. While each field under the umbrella of information science has its own special arrangement of abilities, there are a couple of rudiments that are general. Here are the five abilities you really want to get everything rolling with information science and AI.

Must-Have Skills For Machine Learning

Here we’re going to list down 5 skills that you need to learn in order to start with Machine learning.

1. Linear Algebra

Time to bust out the high school and college textbooks again, because you’ll be needing algebra if you want to excel in data science. Linear algebra involves a lot of vectors and matrices, which are useful in representing large amounts of data – something you’ll see often in your life as a data scientist. Linear algebra is a core skill for deep learning if you choose to go down that path.

1. Statistics & Probability

Statistics involves the collection, analysis, interpretation, presentation, and organization of data. Sound familiar? There are lots of similarities between statistics and data science, such as examining probability, bayesian thinking, experimental design, regression, and so on.

1. Calculus

Uh oh, more math. While you may not need to go back and relearn everything about calculus from when you were 16, you need to understand the core concepts at least. This includes knowing more about gradient descent, linear regression, limits & derivatives, and so on.

1. Data Science

Computer science has been around for quite some time, with a lot of theories and practices making their way over to data science. Many computer scientists make career transitions into data science, so there are plenty of parallels between the two. Core knowledge includes data structures, trees & graphs, lists & dictionaries, and more important skills.

1. A Programming Language

This is where it gets a bit fuzzy since there are debates about what coding language is best for data science. The most common two are Python & R, each with its own strengths and weaknesses. Python is versatile and often used in computer science as well, while R is popular for data analysis. There are many libraries, frameworks, and platforms that use either R or Python, so knowing one language won’t limit you.

Content reference: Open Data Science