Deep Learning, Machine Learning, AI, and Data Science are changing our lives because of their application in self-driving cars, medical devices, robots, automation, entertainment, commerce, and more.

In this article I’ll explain each of these four terms and also look at the immense job opportunities they have to offer.

Firstly, let’s talk about…

Deep Learning

Ever wondered how on earth your Google Assistant or Alexa makes sense of your commands, accents, and questions?

Or, how Google auto-corrects your search queries and shows you exactly what you are looking for?

It’s all due to Deep Learning.

Deep Learning is a subset of Machine Learning that is highly advanced and uses neural networks to make decisions and process data.

Deep Learning uses neural networks and several layers to identify patterns, subtle differences, and details.

For example, a teacher can easily identify numbers and letters written by kids in a class even if the text is extremely different in style. It’s possible due to the human brain’s capacity.

Deep Learning is inspired by the working of the human brain.

Deep Learning uses layers to process data. Each layer has a specific task to perform.

For example, in an image-processing system, one layer might work out the edge of an image, and send output to the next layer, which might identify the key items within the image.

Let’s say the second layer sends its output to the next layer that the image is of a cat. The final layer would map output to the cat category in the system to show the final results.

Deep Learning is used in extremely crucial and advanced systems. For example, autonomous driving companies use Deep Learning systems to identify and process objects on roads and train self-driving car software to differentiate between different objects and obstacles.

Deep Learning is also used in medical applications to identify, detect, and differentiate between different diseases, such as cancer.

Deep Learning systems usually need large amounts of data because it’s less effective to identify patterns with limited data.

Deep Learning also requires high-performance, multi-threaded hardware systems to process data and information.

How You Can Get a Job in Deep Learning

According to an article by The Enterprise Project, based on Indeed’s data, Deep Learning Engineers rank second in job listings seeking AI or Machine Learning skills.

Companies usually hire Deep Learning experts for roles like Machine Learning Engineer, Deep Learning Engineer, and Senior Data Scientist.

On average, Deep Learning experts have a salary of $114,000 US dollars, according to PayScale.

Next, let’s look at…

Machine Learning

There has been a lot of buzz about Machine Learning.  Simply put, Machine Learning is the study of algorithms that learn, evolve, and improve over time using input data, and existing events.

Think about a warehouse where you have a worker whose job is to differentiate between apples and oranges from large containers and put them in separate boxes. Suppose you want to automate this job to increase efficiency. How would you program your system?

If the object’s color is orange, its surface is smooth and round and its weight is 140g on average, tag that as an orange. If the object is yellowish or reddish and weighs around 100g, tag that as an apple.

In ideal circumstances this system would work, but what if there is an orange weighing 180g? Would your system be able to identify it?

Yes, if you implement Machine Learning!

Machine Learning-based systems are able to improve their performance and accuracy as we feed them data. More data (often known as “training data”), equals more accuracy.

Machine Learning is a subset of Artificial Intelligence and aims to train computer systems for accurate predictions, decision making, automated tasks, detection, identification, and performance without human intervention.

To see machine learning in action, look at:

• Computerized systems that detect oil underneath the Earth’s surface
• Medical systems to detect cancer and other diseases in early stages
• Stock market prediction systems that use past data and hidden patterns as input
• Speech recognition systems
• Chatbots, and
• YouTube’s recommendation algorithm that uses your consumption habits and patterns as input

How You Can Get a Job in Machine Learning

The Machine Learning industry is highly competitive. If you want to start a career in Machine Learning, start working on your programming and data analysis skills, build beginner-level language processing systems, learn data pipelines, and solve complex problems in data and image processing.

There are a lot of PhDs in the Machine Learning industry as the link between academia and Machine Learning technology is very strong.

However, there is demand for experts who are able to use programming and the latest tools to solve complex Machine Learning problems.

If you can combine theoretical expertise, for example, math, statistics, and image processing, with programming skills, you will flourish.

According to the Robert Half Technology 2020 Salary Guide, Machine Learning experts can earn over $163,000 US dollars.

Companies hire Machine Learning experts for a variety of roles, such as Big Data Engineers, Data Scientists, Data Modelers, and Software Engineers.

Ok, now let’s learn about…

Artificial Intelligence

When a device starts perceiving its environment and takes action based on its own decisions, it’s using Artificial Intelligence.

While Machine Learning is about creating systems that learn from data, AI is about creating intelligent systems.

For a system to be AI-based it should have the following characteristics:

• Natural Language Processing: It should process language data and differentiate between different responses.
• Knowledge Representation: It should store, remember and index when it comes across different events in its lifecycle.
• Automated Reasoning: It should use stored information to draw conclusions, answer questions and create connections between objects.
• Machine Learning: It should learn from new data and be able to adapt to changing or evolving situations based on new data and input.

Simply put, AI is a computing system that is able to perform tasks that usually require human intervention or decision making.

In the words of Stuart Russell and Peter Norvig, who wrote the famous book Artificial Intelligence: A Modern Approach, for a computing system to be qualified as intelligent, it should think and act “humanly” and “rationally.”

Narrow AI

The AI we currently see in action worldwide in advanced systems, autonomous cars, search engines, robots, and medical systems is based on a lot of constraints and controls programmed by humans. This type of AI is called “Narrow” AI.

Artificial General Intelligence

The sort of AI you see in dystopian movies and fiction where robots and computers have total control and can supersede humans in decision making is called Artificial General Intelligence.

How You Can Get a Job in AI

AI forms the basis of Machine Learning, Data Processing, Decision-Making Systems, Automation Software, and NLP systems.

There is huge demand for experts who could work on AI-based software and hardware.

AI has deep academic roots. Therefore, having a PhD will be a strong plus if you want to enter the industry.

Careers in AI usually start with Machine Learning and Data Science roles. You should have strong programming and statistics skills. You should also have experience working with NLP systems to enter advanced AI roles.

AI is a vast field and there are a variety of roles for which companies hire Artificial Intelligence experts.

Just a simple Google search will show you tons of AI jobs for roles like Deep Learning Expert, Machine Learning Engineer, Software Engineer, Automation Engineer, Research Director, and Neural Networks Engineer.

According to PayScale, AI has an average salary of over $123,000 US dollars.

Lastly let’s look at…

Data Science

The art of making sense of data is called Data Science.

A Data Scientist usually excels at processing large chunks of data and doing analysis to solve a problem, predict a scenario, and find patterns that are helpful for businesses.

Data Science mainly involves two types of analysis:

• Predictive analysis, and
• Prescriptive analysis

Predictive analysis involves analyzing existing data and forecasting or modeling for specific events.

For example, a bank could hire a Data Scientist to process data and analyze the possibilities of its customers defaulting on their loans.

Prescriptive analysis involves making computing systems that can make their own decisions based on data.

You can change prescriptive analysis models based on dynamics parameters. This form of data science has much in common with Machine Learning and AI. It is used to train algorithms to make decisions based on possibilities.

Not every Data Scientist does the same job. There are numerous applications of Data Science.

A Data Scientist usually spends his day creating data models, coding, making database schemas, making ETLs, querying data, analyzing data, making plans, debugging code, and solving mathematical problems for data modeling.

Suppose a pharmaceutical company has tons of patient data and it wants to make an algorithm that would predict chances of diabetes in a patient in the future.

The first task of the Data Scientist would be to clean the available data. Data could be in any form  —logs, cloud data, SQL, NoSQL, or text.

To perform any kind of analysis, you need to analyze your data. After sorting the data, you will start processing the data to look for trends. This will be done using programming.

In the end, you will create a decision tree that will identify the probability of a patient getting diabetes based on different possibilities. Lastly, you will come up with a data model or algorithm.

How You Can Get a Job as a Data Scientist

To become a Data Scientist you should have strong programming skills.

Cleaning and analyzing data using programming languages like R, Python, VB, C++, and C, plays a key role in most Data Science jobs.

You will also need to use some tools for data cleaning and processing, such as RapidMiner, BigML and Weka.

Some companies and organizations also require their Data Scientists to solve mathematical problems.

Strong math skills will help you solve complex data modeling problems.

Data Science is being widely adapted by companies in major industries like pharmaceutical, defense, education, technology, food, and electronics.

The average salary for the Data Scientist role is $96,000, according to PayScale.