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The job market is changing constantly with many vocations that were once considered respectable and career-worthy now all but redundant. Similarly, roles that didn’t even exist twenty years ago are now mainstream. It is very hard to know what will work and what won’t in twenty years’ time and for those school-leavers who are preparing to study and enter the world of the employed that is very daunting. Nobody wants to study for four or five years only to discover that the field they were intending to specialise in has been taken over by robots or AI or is simply completely superfluous. With that in mind, here are a few roles that look set to dominate for many years to come.

Learn code

Code is another way of talking about computer language, and there are many of these. A great place to start is by learning to code. If you have some coding skills in your draw that will immediately open up opportunities for you. Not necessarily as a coder, because even that is rapidly losing its cachet and speciality. For many coding is now about accessing libraries of previously written code and compiling it together in a new way to create something different. There are also plenty of app developers Australia or computers coders in New Zealand. The market is full of skilled developers, so there are no guarantees that you will find work as a developer. But being able to code and understand a coding language or languages is good as the field of technology is varied and wide. Think of it as being able to speak Chinese… If you are fluent in Mandarin you are not guaranteed a job, but it opens up the world of opportunities in Chinese related business. The same applies to code.

Independence

One of the biggest changes that have been brought on by the advances in technology in recent times is the shrinking of the world and the democratisation of media. Now anyone can have a voice and you can work for people anywhere in the world. The old idea of getting into a car and heading to the office for eight hours before trudging home again is well past its sell-by date. Now it is about freelancing and creating your own brand. Think YouTube channels and Social Media influencers. Start getting used to the idea of working for yourself; of being a digital nomad and start looking for angles that will help you stand out and succeed.

Conservation

The young generation is far more aware of issues like global warming and the environment that previous generations ever were. Youngsters like Greta Thunberg are leading the way with their activism and millions of young people are behind the idea of taking control of their futures. So, look at studying ecologically focussed courses. Think organic and ethical and conservation. These are the types of endeavours that are going to be supported and needed for decades to come – because the state of the earth is going to get worse still before it starts to get better.

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Machines have been an integral part of our lives for years now. And now with machine learning and AI, it has become possible for machines to work independent of human intervention.  Progress in this space now allows machines to carry out tasks as diligently and intelligently as humans do, with high accuracy, if not completely perfect. We are quite literally trying to build a brain for machines, and that can be done only if machines are taught to learn the way we do. No wonder machine learning has found such a huge following in the world today, both by its creators and the users.

As with any domain, there is always a tussle and debate around the right way to build a particular software and the programming language which is the best fit for it. The domain of machine learning is not an exception either. And while the tide keeps shifting every once in a while, major support for Python has stayed in place consistently through the years.

Here’s why a Machine learning with Python course is absolutely essential for you if you’re looking to make your mark in the machine learning space and get a job that helps you make it happen.

1.Reusability of code and ease of configuration

Machine learning, in itself, is quite a complex challenge to begin with. Understanding the logic, recognizing patterns, and developing code that can help keep it all together is even more difficult than it might seem at the outset. The last thing any programmer would want is a language that makes things even more complex than they need to be.

Python comes with a good collection of libraries that serve a major purpose of code reusability. Not only are these libraries filled with instructions of how to use them in the best way, but simple tweaks in their modules can help them to be reused for different functionalities within the same model. Configuring them is as easy as breaking them down, testing them, making changes, and rebuilding them, all within a single space. Compare this with other languages and many programmers struggle with challenges like multiple structures, syntax differences, and so on.

Image by Johnson Martin from Pixabay

2.Identifying and correcting errors quickly

We all know that machine learning is not going to be a simple task. There will be multiple iterations of building the algorithm, carrying out tests, and verifying results. What could be detrimental in this aspect is if there’s no way to figure out why the results are incorrect and trace them back to the code in order to understand the area responsible for the failure. Even popular languages like R, that boast extensive application capabilities, don’t make this an easy task, requiring hours and hours spent in simply detecting a small error.

Contrast that with Python that is extremely handy with error reporting which is specific and right on point. A few clicks can take you right to the module that caused the error and allow you to get down to the root cause in a considerably shorter period of time.

3.Machine learning, and not machine solving

The goal of machine learning is to create logic and algorithms that help the machine to learn by itself. We are not looking for results on what the machine is doing and what output it brings out. That enters the realm of research in evaluating machine performance.

Python helps programmers focus on the core aspect of machine learning. It is highly reliant on real-time processing and analysis of data, which allows the machine to make changes in its behaviour. This aspect of helping a machine gain insight into its own working is deftly achieved by using Python than any other language.

4.Wider applications that support machine learning

Picking up Python as one of the primary programming languages to be proficient in has a lot of advantages that go beyond the world of machine learning, but ultimately feed right back into it. It helps you be more aware of the different logic principles that govern code functionality and make you a better programmer. This helps pick up other languages speedily and code them in an effective manner.

A lot of other resources are also essential in understanding the nuances of machine learning, and these may make use of programming components that are independent of the domain. However, most of these are also built using Python, so knowing the language beforehand can always help you understand them better.

5.A high demand for Python in major job markets

Currently, the United States is one of the biggest markets where machine learning developments are at an all-time high. A look at job portals, makes it evident that many companies are on the lookout for good Python programmers that understand the nitty-gritty of machine learning and can develop excellent solutions.

In the coming years, this trend is just going to flow over to markets all across the world. So even if you don’t have immediate plans of going to the US and applying for a job, preparing for the inevitable boom in the sector is always in your benefit.

Learning other languages like SAS or R is a good decision if you have set your mind to enter a specific retail space that makes use of them widely. However, a good Machine learning with Python training course is your best choice for staying in step with the changing times and being future ready.

Since the development of computers or machines, their competence to enforce various tasks went on growing epidemically. Humans have advanced the power of computer systems in terms of their disparate working domains, their proliferating speed, and decreasing size with respect to time.

An arm of Computer Science named Artificial Intelligence goes after creating computers or machines as brilliant as human beings.

As we go ahead with the entire career prospective of Artificial Intelligence, let’s understand briefly about what Artificial Intelligence is.

A Brief Overview of Artificial Intelligence

The father of Artificial Intelligence, John McCarthy said, “Artificial Intelligence is the science and engineering that makes intelligent machines, exceptionally intelligent computer programs”.

AI makes a computer, a computer-controlled robot, or a software think intelligently, the same way an intelligent human would think.

While capitalizing the competence of the computer systems, the eagerness of human, lead him to doubt, “Whether a machine thinks and behave as humans do?”

Hence, the advancement of AI started with the objective to create similar intelligence in machines that we humans could. Artificial Intelligence aims to create expert systems. These systems present intelligent behavior, learn, display, explain, and advice their users. It implements human intelligence in machines by creating systems that understand, think, learn, and act like humans.

Image source Pixabay

Career Prospects of Artificial Intelligence

A big core of AI is in the advancement of computer functions associated with human intelligence, such as reasoning, learning, and problem-solving.

AI has been assertive in various fields such as −

  • Gaming − It plays an important role in critical games such as chess, poker, tic-tac-toe, etc., where a machine can think of a large number of possible positions based on probing knowledge.
  • Natural Language Processing − It helps in interacting with the computer that understands the natural language which humans speak.
  • Speech Recognition − Some intelligent systems are able to hear and comprehend the language in terms of sentences and their meanings while a human talks to it. They can handle different accents, slangs, background noises, change in human’s tone due to cough, etc.
  • Intelligent Robots − Robots perform tasks which are assigned by humans. They can sense physical data from the real- world such as light, heat, temperature, movement, sound, bump, and pressure. They have enough processors, various sensors and vast memory, to display intelligence. Also, they are able to learn from their mistakes and they can adapt to the new environment.

Domains which target Artificial Intelligence are as follows:

  • Medicine: includes identification of medical images, diagnosis, expert systems to aid GPs, monitors, and controls in ICU,  designing of prosthetics, and drug designing.
  • Robotics: includes vision, motor control, learning, planning, linguistic communication, cooperative behavior.
  • Engineering: identification of fault diagnosis, intelligent control systems, intelligent manufacturing systems, intelligent design aids, integrated systems for sales, design, production, maintenance, expert configuration tools (e.g. ensuring sales staff don’t sell a system that won’t work.
  • Space: It controls space vehicles and autonomous robots too far from earth to be directly manipulated by humans on earth, because of transmission delays.
  • Marketing: AI is being used to develop more targeted, relevant, and timely marketing programmes to enhance customer attrition rates. Examples of typical jobs held by AI professionals include:

1.Software analysts and developers.

2.Computer Scientists and computer engineers.

3.Algorithm specialists.

4.Research scientists and engineering consultants.

5.Surgical technicians working with robotic tools.

6.Medical health professionals working with artificial limbs, prosthetics, hearing aids, and vision restoration devices.

7.Military and aviation electricians working with flight simulators, drones, and armaments.

Salary Scale: The average salary of an Artificial Intelligence Engineer is approximately $93,625 per year depending upon the domain you choose.

Advance information technologies and the onset of machines enhanced by Artificial I

Intelligence (AI) have already influenced the world of work in the 21st century. Computers, algorithms, and software cut down everyday tasks, and it is absurd to imagine how most of our life could be managed without them. Nonetheless, is it also futile to imagine how most process steps could be managed without human effort. If they are then Artificial Intelligence will be an assured short movement in IT industry?

To learn and master in Artificial Intelligence, you can go through various AI Certification Courses available online.