Mastering Machine Learning – 5 Skills That Can Get You There

If you are a technology enthusiast, a career in machine learning sounds exciting. The prospects are lucrative, and you get to work with the latest technologies like Artificial Intelligence and Data Science. But mastering machine learning goes beyond exploring these tech buzzwords, as you need to have an extensive skill set to unlock success. You have to bring a complete package of technical and soft skills to make it big. Let us explain the ones that can help you build a dream career with machine learning. 

Applied mathematics

Let’s begin with the basics! A solid understanding of Applied Mathematics gives you the best foundation for a career in ML. You will need to apply various mathematical formulas to select the correct ML algorithm, so this is a skill you cannot manage without. ML algorithms require knowledge of key concepts such as statistical modeling, vector projection, multivariate calculus, and probability. Apart from mathematics, some understanding of Physics concepts gives you a good start. 

Computer programming 

Another essential skill for aspiring machine learning engineers is computer programming. You must be familiar with computer science concepts such as algorithms, data structures, and space and time complexity. Knowledge of ML-specific programming languages like Python and R gives you an advantage. You must also know other key programming concepts such as SQL for database management, Hadoop and Spark for distributed computing, and Apache Kafka for data pre-processing.

ML algorithms

This one is a no-brainer because you will surely need to know ML algorithms to explore a career in this domain. A good understanding of the common machine learning algorithms enables you to pick the right one for the right applications. They are categorized into Supervised, Unsupervised, and Reinforcement algorithms. The common ones you will come across often include Naïve Bayes Classifier, Support Vector Machine, Linear Regression, Logistic Regression, Random Forests, and Decision Trees. 

Data modeling

You cannot overlook data modeling and evaluation skills because you need to work with data for every single ML application you create. Data modeling requires you to know the underlying data structure and detect patterns that are not visible to the naked eye. Data evaluation involves the use of an algorithm suitable for the data. Matching the right algorithm with data is the key to effective data modeling and evaluation.

Soft skills

Apart from these technical skills, you must also have some attributes that set you up for success in this challenging landscape. Being a self-learner helps because the field is constantly evolving and you need to upgrade yourself continuously to keep pace. Dedication, hard work, and motivation are some other qualities that make you successful. You will also need excellent communication skills to understand the expectations of clients.

Reaching the heights in the machine learning domain takes a lot of hard work, but the effort is worthwhile. A successful career opens you up to the most incredible opportunities, where you can make money and do what you love.