5 Must Have Skills Needed For Machine Learning Jobs

Rajarshi Mitra

1 year ago

Machine Learning is one of the hottest and most disruptive technologies out there. Machine learning jobs are in extremely high demand. More and more companies are adopting these technologies and this demand is only going to go higher. In this article, we are going to look at the skills needed to get these jobs.

But before that, let’s understand the basics of machine learning.

What is Machine Learning?

First and foremost, what is machine learning?

Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. In layman’s terms, it is the knowledge source that a computer system uses to become more intelligent.

So, what are the different kinds of learning systems out there? Well, they fall under four distinct categories:

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning

Supervised Learning

In supervised learning, you feed the machine a series of inputs and its corresponding outputs. The idea is to get them accustomed to the kind of outputs they can expect from certain inputs so that they eventually learn for themselves as to what output they should give to future inputs.

[Input/Output Set] —–> [Machine Algorithm] ——> [Working Model]

This is basically classic spoon feeding. You are telling your machine to learn how to identify specific outputs for inputs. Think of how you learned maths when you were a kid. You went through some examples and then solved new problems of your own.

Unsupervised Learning

In unsupervised learning, you simply feed a set of inputs to the machine without giving them the required outputs. The idea is to help your machine recognize and identify different patterns in the input and cluster them according to similar data.

So, if you feed 1, black, 3, red to the machine, then they will cluster them as:

{red, black}, {1,3}

So, the skeletal framework of this machine will look like this:

[Input Set] —–> [Machine Algorithm] ——> [{Cluster 1}, {Cluster 2}….{Cluster N}]

Semi-Supervised Learning

Somewhere between the supervised and unsupervised learning, we have semi-supervised learning. Semi-supervised data is a combination of labeled and unlabeled data. Labeled data are the inputs which have the corresponding outputs, while unlabeled data has inputs with no outputs.

Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy over unsupervised learning (where no data is labeled), but without the time and costs needed for supervised learning (where all data is labeled).

Reinforcement Learning

Finally, we have reinforcement learning.

In this kinda learning, we have an agent who interacts with the environment by committing an action. This action changes the state of the environment and depending on whether the state is good or bad, the agent will either get a reward or a punishment.

We have ourselves faced this kind of learning multiple times in our life. Whenever we did something good, we got rewarded, while every time we did something bad/wrong, we got punished. This taught us which actions were the right ones to take and which ones weren’t.

So, that should give you a fair idea of what machine learning is about.

The Rise of Machine Learning Jobs

Analytics India Magazine revealed that in 2017, around 78,000 jobs in the Data Science and Machine Learning space were lying vacant in India. These numbers suggest that both ML Engineers and Data Scientists job roles are much in demand in the analytics community.

LinkedIn also did an interesting study regarding this rising demand. Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn. Since 2012, Data Scientist roles have increased by 650% and it is estimated that there will be 11.5 machine learning engineering positions by 2026, according to the U.S. Bureau of Labor Statistics.

Here are some of the points of interest from their research.

NOTE: The study was done in 2017.

While calculating the rate of growth of jobs, they found out:

• There are 9.8 times more machine learning engineers working today than 5 years ago.
• There are 6.5 times more Data Scientists than five years ago, and 5.5 times more Big Data Developers.

The study also shows that Software Engineering is a common starting point for professionals who eventually get into Machine Learning and Big Data.

The top five highest growth job typical career paths are shown below:

What Is Making Machine Learning Engineering Role So Popular?

The following graph shows the rise in demand for machine learning/artificial intelligence jobs, according to Indeed, the popular online job portal.

So, what is the secret behind this growth? Why have machine learning jobs gained so much popularity within such a short time frame?

Well, that’s because many top companies are incorporating ML and AI into their systems! It really is that simple. As more and more companies look to get into this space, they are looking to invest in and hire more machine language experts to put themselves in front of their competition. According to this article by Forbes, the number of machine learning patents increased between 2013 and 2017 by a rate of 34% CAGR.

To further drive home the importance of machine learning in this day and age, here is another neat little fact for you. Majority of these patents have come from companies such as IBM, Facebook, Microsoft, LinkedIn, Intel etc.

Alright, so what have we learned so far?

• Machine learning and Ai-based jobs are on the rise.
• Machine learning is the study of algorithms that help our machines learn and get smarter.
• Some of the top companies in the world are actively looking to hire experts in this area to gain an edge over there competition

So, the next question is, what skills do we need to become an expert in this area? Well, let’s take a look.

5 Skills Needed For Machine Learning Jobs

#1 Programming Fundamentals

Computer science and programming fundamentals are absolutely essential for machine learning and artificial intelligence. It is extremely important to have some degree of proficiency in data structures, algorithms, computability, complexity, and architecture. The five languages of choice are Python, R, JavaScript, Java, and C++.

• If your expertise in machine learning is around sentiment analysis, then you should prioritize Python and R.
• If you are interested in natural language processing then you can use the vast library Python has to offer to create high performing algorithms
• If network security, fraud detection is your area of interest then you should probably look into Java
• C/C++ is usually used to incorporate AI in games and robot locomotion as those are the fields where an extremely high level of control, performance, and efficiency is required
• R is heavily prioritized in areas like bioinformatics and bioengineering
• Developers who are new to data science and machine learning seem to prioritize JavaScript and Java.

Image Credit: Developer Economics

You can read our guides to learn python and javascript.

#2 Mathematics

Mathematics, and especially probability and statistics, are an essential cog of machine learning. It is extremely important for you to gain a thorough understanding of machine learning. At the very heart and soul of machine learning lies a formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.

An understanding of probability and statistics will let your machine deal with uncertainty in their decision-making process. You need to know which algorithm to use for what purposes and how to make modifications based on changes in the environment. Probability can be very useful in that regard. Statistics, on the other hand, will give you the measures, distribution, and analysis methods needed for efficient building a robust model from the observed data.

#3 Machine Learning Algorithms

It goes without saying that you need to be proficient in machine learning algorithms to be proficient in machine learning. You need to understand how linear regression, logistic regression, SVMs, gradient descent, quadratic programming etc. However, just knowing what those algorithms are is not enough, you should also know when to apply what. Each approach has its own advantages and disadvantages and you should know how to navigate the algorithms without tripping. You can develop this instinct only via practice.

#4 Data Modelling

Data modeling and system design is the next skill that you need to master. According to Wikipedia,

“Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.”

To simply put it, data modeling will help your machine in estimating the underlying structure of a given dataset, with the goal of finding useful patterns and/or predicting properties of previously unseen instances.

Data modeling is used to model data in a standard, consistent, predictable manner in order to manage it as a resource. Any kind of project, like machine learning, which requires a standard means of data analysis to:

• Assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and clients to understand how the concepts of the organization work and how they connect to each other.
• Manage data as a resource
• Integrate different information systems
• Design databases

The estimation process helps in continually checking how good a model is and what all needs to be done to improve it further. Understanding these processes is critical in the application of machine learning algorithms.

#5 Software Engineering

“the systematic application of scientific and technological knowledge, methods, and experience to the design, implementation, testing, and documentation of software.”

While creating a machine learning model, you will need to understand how different components work and communicate with each other. This is why you should know what all to do to avoid unnecessary overlaps and letting your algorithms scale as the amount of data in the system increases.

Bonus: Learn Learn Learn

Machine learning and artificial intelligence are rapidly growing fields. Every single day there is a new discovery or use-case which has industry disrupting potential. You need to constantly keep reading and learning to gain more and more knowledge. That is the only way that you will make sure that your skills aren’t getting outdated.

Conclusion: Machine Learning Jobs

So these are the skills you will need to get the top machine learning jobs. With the rise in demand for machine learning jobs, you need to do everything in your power to position yourself as an expert. Machine learning isn’t some technology that is “on its way”. It is already here and companies have already started utilizing it to improve their daily operations. With the sheer shortage of talent, now is the best time possible for you to get started

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