Master machine learning — and snag a great job — with these key job skills

If you’d go by the marketing newsletters of leading IT solutions vendors of the world, it would appear that artificial intelligence and machine learning are ideas that have come into being, almost magically, in the past two to three years. That’s untrue, of course. Artificial intelligence, in fact, is a term that was coined way back in the 1950s by computer programmers and researchers to describe machines that could respond with appropriate behaviors to abstract problems without human input. Machine learning is one of the more prominent approaches to making artificial intelligence a reality. It is centered on the idea of creating algorithms that are inherently capable of identifying patterns in data and improving their outcomes based on the large datasets. This guide is dedicated to helping you understand and identify the fundamental skills you need to master machine learning technologies and find fulfilling employment in this hot and growing field.

What makes machine learning so special as a career choice?

Netflix and Amazon are the perfect examples of how machine learning can create experiences that make machines look intelligent, much like humans. ML is at play when Netflix seems to magically suggest movies and TV series that pique your interest. Amazon can cross-sell with confidence because the products it suggests for you are a result of super-smart algorithms working on terabytes of data. Consider the extent and expanse of business-use cases where ML can be leveraged to disrupt the status quo. It’s obvious — machine learning is the most exciting career option for anybody looking to make a choice, as well as for those looking for a new lease of life for their IT careers.

Flexibility with programming languages

The biggest question troubling most machine learning enthusiasts is — should I learn Python, or should I learn R? These two are the most commonly used programming languages in developing machine learning algorithm-based programs. However, for developers looking for sustainable success in the market, this is not the right question.

• Different languages offer very specific advantages that make solving AI problems easier; you don’t want to stonewall the project.
• There are libraries available that make it easy to develop ML-based programs in any language.
• There are things that certain ML technologies can’t do; programmers need to be aware of alternatives in such cases.
• All the while, there are new approaches to realizing machine learning algorithms coming up. To be able to stay in the game, you’ve got to be able to adapt and master machine learning technology as it changes.

Probability and statistics

The basics of probability are at the core of machine learning. Bayes rules, conditional probability, likelihoods, independent and interdependent events — all these are compulsory elements of the probability theory knowledge of anybody interested in a fulfilling career in machine learning. These concepts are the scientific basis of dealing with uncertainty in machine learning algorithms. Concepts of statistics are very closely linked to these probability concepts. Mean, median, mode, variance, binomial theorem, etc. are crucial for any programmer or data scientist to be able to validate and master machine learning algorithms. In fact, many machine learning algorithms are logical extensions of statistical modeling procedures.

Data modeling

Data modeling is the science of identifying the underlying structure of complex datasets. The success of machine learning algorithms depends on finding useful patterns in datasets. These patterns could be correlation, eigenvectors, or categorizations. Also, data modeling aims at finding the properties of previously unseen instances, such as anomaly detection, regression, and classification. Continual improvements in data models are at the core of machine learning. Depending on the inherent tolerance of the application for which the model is being designed, you will need to decide on a measure of accuracy or error. Iterative learning algorithms must be able to use error measure to tweak the model. Even for developing basic machine learning algorithms, you will need data modeling basic knowledge to be able to find your way.

Why you must also master machine learning libraries

Here’s something — for most basic and standard machine learning implementations, there are libraries, APIs, and packages available. However, for anybody to succeed in machine learning projects, it’s imperative they are able to:

• choose a suitable model (nearest neighbor, decision trees, neural networks, mix of multiple models).
• choose a learning procedure to fit the data.
• understand how hyper-parameter affect the algorithm’s learning.
• evaluate the pros and cons of different realization approaches.

Machine learning and data science challenges available on Kaggle are a great method for enthusiasts to learn the nuances of the trade.

Dexterity with data

The big secret to master machine learning is that data always precedes algorithms in terms of importance. A great machine learning-powered program can be realized from great data, even if the algorithm used is basic. So, anybody looking to build a career in machine learning would do well to focus on data management, data governance, and data analytics before diving deep into advanced algorithms development. Machine learning algorithm programmers have to spend a lot of time in data transformations, which makes this a key aspect of their work.

Distributed computing

Machine learning entails work with massive datasets. It’s not practical for programmers to work with a single machine’s resources to carry out the gigantic workloads of data processing in machine learning. This is where distributed computing comes up. Any experience that you have with distributed computing can prove to be invaluable here. Apache Hadoop and Amazon EC2 offer good solutions for distributed computing, so you might want to understand the basics of these technologies.

Machine learning is the one force that’s powering technological applications powerful enough to disrupt existing industries. Already, enterprises across the globe have started alloting hefty budgets for developing their in-house machine learning capabilities. The range of career options and employment opportunities that this opens up for you is very large and wide. Use the tips and advice presented in this guide to master machine learning and plan your next move.