Must-Have Skills To Achieve Mastery In Machine Learning
By: Cognitior Learning
Tags: Machine Learning, Data Science, Programming Languages, Programming, Python

PC: Cognitior

Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It is one of the most exciting technologies that one would have come across.

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. It has given us self-driving cars, practical speech recognition, effective web-search and much more.

Machine learning is a combination of Math, Data Science, and Software Engineering, so to become a Machine Learning Engineer you need to be proficient in all the three fields.

Also, you have to do very well in the following basic domains:

Probability Statistics Data Modeling

Programming Skills

Machine learning involves creating dynamic algorithms, which means you need to master programming and software development. Moreover, you have lots to do with the fundamentals of analysis and design. Here’s what you need the most:

1. Fundamentals of Programming

Machine learning involves performing calculations on huge sets of data, and henceforth it requires proficiency in the concepts of Data Structures, Algorithms, Complexities, and Computer Architecture.

Just review stacks, b-trees, sort algorithms and practice to solve programming problems around these.

2. System Design

As a machine-learning engineer, you need to have a strong background in Application Programming Interfaces (API) of different kinds such as web APIs, static and dynamic libraries etc.

You must be able to create good software design. As your algorithms will be dealing with huge data, so you have to ensure that they can handle everything and not stumble or choke.

Also, you need to have testing, documentation, and design skills to ensure quality and maintainability in long run.

3. Machine Learning Libraries & Algorithms

Learn to implement machine-learning algorithms through libraries, packages, APIs etc. and apply them effectively using standard models of decision trees and neural nets.

Models like Genetic Algorithms, Linear Regression, Bagging and Boosting are also an integral part of ML.

4. Programming Languages

Machine learning isn’t bound to any specific language and just like object-oriented programming; it can be implemented in any language that has the desired components and features.

Generally, Python and R are the favorable languages for Data Scientists and Machine Learning Engineers.

Python has been the most favorite language among specialists as it has a number of useful libraries for efficient data processing and scientific computing.

R has been specifically tailored for statistical computing and data mining, making it an excellent choice for machine learning tasks.

Machine Learning Everywhere — The Future of Businesses

PC: Cognitior

What is most efficacious about machine learning is — there are virtually NO FIELDS to which machine learning doesn’t apply. There has been so many fields being impacted by it including education, finance, computer science, manufacturing, travel, energy, and more. Healthcare is an obvious example where machine-learning techniques are desperately needed.

The world is changing rapidly, and the demand for Machine Learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them.

So, as you might have seen now, Machine Learning is everywhere!

And if this is your future, there’s no time like today to start mastering the skills you need to succeed.