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How to Build a Career in AI and Machine Learning

February 14, 2019

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There are a
plethora of job roles that fall under data science but the most common field
that we often hear about is — Artificial Intelligence and Machine Learning. As
the competition strengthens the job market, employers are still struggling to
find the right talent. Every company today, be it financial services or
e-commerce companies they all are looking to hire the best talent skilled in
artificial intelligence and machine learning.

But it is
very important for one to first understand the difference between these two job
roles. Artificial Intelligence and machine learning have become an integral
part of many businesses. However, the terms are used interchangeably, let us
look at the differences.

The differences between AI and Machine Learning

Artificial Intelligence

Artificial
intelligence is all about computer intelligence. In simple terms, it is a
broader concept of machines that carry out tasks in a way that is considered –
smart. As technology advances, computers are trained in such a manner that they
understand how our minds work. You probably have seen it unfold right in front
of your eyes, from self-driving cars to Google brain it is because of the
impact of artificial intelligence.

In short, artificial
intelligence is a study that develops machines and software exhibiting human
intelligence.

The role of an artificial intelligence engineer, specialist etc. is to program computers to be able to test the hypothesis in relation to how the human brain works, through cognitive simulation. They cover various aspects that range from facial recognition, recognising the voice that helps solve complicated problems.

To start a career in AI, this is how you should go about it:-

Learn a programming language – Python is the most favorable language in AI, you can also code AI applications in Java or C++ etc. Linear algebra and calculus

Build your first AI bots

AI is a huge field, you need to first learn the subjects that fall under the sub-field like –

Neural networks

Robotics

Evolutionary computation

Speech processing

Expert systems

Machine learning

Natural language processing etc.

Machine learning

Machine
learning is a sub-field of AI, machines take the data that is retrieved and
learn it for themselves. It is one of the promising tools in AI today. Machine
learning helps the system learn and recognise the pattern on its own to further
make predictions.

A Machine
learning engineer is neither a data scientists nor a data engineer, he is one
who sits in the crossroads of both these job roles. They are engineers who are
cross-trained to become proficient both with data engineering and data science.
A machine learning engineer takes what a data scientist finds and make it
production worthy by using algorithms which can be either an ML or an AI code
to instill results. If the results go haywire, incorrect or distorted either
way a machine learning engineer will the one who will be on the lookout to make
changes in their model that would require tweaking or retraining.

To start a
career in machine learning, this is how you should go about it:-

Learn
programming languages like C++, this can help speed your coding skills. R is
used for statistical problems.

Learn
Statistics

Machine
learning algorithms

Data
modeling

Data
evaluation

Distributed computing

How can you
start your career in the latest trending technologies?

Update
yourself by taking up certification courses and credentials in technologies
such as artificial intelligence and machine learning. But unless you already
have a strong quantitative background the pathway to these careers can be
challenging but not impossible.

Here are some great certifications credentials you can consider learning from:

Cloudera – Cloudera covers
these topics under the data science program. Participants of the program or
workshop must have a basic understanding of concepts such as machine learning
algorithms, Python, R, and statistical modeling etc.

Coursera – The Coursera
course covers topics such as data mining, statistical pattern recognition,
supervised and unsupervised learning, machine learning etc. The course also
discusses topics such as how to apply algorithms in building robots, text
understanding, database understanding etc.

Artificial
Intelligence Board of America – The Artificial Intelligence Board of
America (ARTIBA) provides the credentialing framework that most organisations
look for when hiring. Since it is quite difficult for an employer to believe
the skills the candidate possess it is important that one needs to showcase
their skills through the credentials that one acquires. The skills you learn
covers Machine learning, Neural networks, Regression methods, NLP, Cognitive
computing and Deep learning etc.

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