Artificial Intelligence, or AI, is a pivotal component of today’s technological revolution. The real-world applications of AI have grown multi-fold over the past decade, bringing forth technologies like speech analysis, NLP (Natural Language Processing) and computer vision. Its impact on society and industry has only grown and will continue to grow in the years to come.

With this perspective, it’s easy to see how and why the AI domain has been teeming with employment, earning, and innovation opportunities. As of today, several people can answer what AI is, but not many are qualified to work with AI or make any strides in the field. There are more AI jobs today than candidates qualified to fill those vacancies - This is good news for candidates who are just starting or aspiring to work in the AI domain. Multiple open positions mean employment is practically guaranteed once you complete a course in AI.

Is the Artificial Intelligence Career Path Worth it?

The AI domain is one of the most fruitful IT domains today. According to predictions by the United States Bureau of Labor Statistics, the number of mathematical and data science professionals critical for all AI and data-related roles will grow by 21.4% by 2030.

In AI, there are several subdomains like computer vision. It also has entire subsets within itself. One example is machine learning, which teaches machines how to improvise, learn and perfect their skills without human intervention. Jobs in the machine learning domain are predicted to grow by 40%, reaching a valuation of $31 billion by 2024.

Artificial intelligence enables and equips candidates to work in several domains, all delivering life-altering tech-driven solutions. From allowing self-driving vehicles to analysing raw data to diagnosing diseases and running tests, AI has real-world applications across almost every industry in some form or other today. It is also one of the highest-paying job roles globally, providing a $125000 per annum base salary on average. Hence, the Artificial Intelligence career path is ever-evolving for professionals to have a dynamic career in AI. 

Applications of AI Today:

Some of the industry-wise applications of different artificial intelligence skills are:

• Healthcare: Ensures proper diagnosis, treatment and digital patient record storage
• Education: Furnishes suitable learning environments with access to a wide range of information and resources
• Agriculture: Enables maximum yield by developing ideal farming environments based on the produce
• Sports: Helps athletes maximise their abilities, and governing bodies monitor and implement rules
• e-Commerce: Enables effective warehousing, relevant product recommendations and online security
• Banking: Implements effective fraud detection, payment security, chatbot assistance and customer data storage
• Construction: Enables safer construction conditions, ensures worker safety, identifies areas of improvement and enables effective communication

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Artificial Intelligence Skills You Need:

AI is designed to mimic multiple aspects of average human intelligence and behaviour. These can include image recognition, speech recognition, facial detection, biometric recognition, natural language processing and recommendation systems. On the other hand, a conventional method of programming systems to appear like human behaviour was limited to image or speech recognition. The newest way to explore and practice human behaviour is by implementing technologies like Deep Learning and Machine Learning.

The two key subsets that help implement or achieve Artificial Intelligence today are Machine Learning and Deep Learning.

Machine Learning:

Machine Learning is the method that computers use to understand and learn from incoming data without the need for complex preprogrammed rules. ML algorithms create models based on training or sample data, significantly large datasets. These models then help in making predictions and taking decisions without explicit rule programming or modification, giving them a significant advantage over conventional methods of programming.

Deep Learning:

The Deep Learning domain is based on the use of artificial neural networks. It is a specialised technique of machine learning derived from human neural networks. 

Artificial Intelligence and Coding:

Machine Learning and Deep Learning both require extensive libraries for successfully handling and processing data effectively and efficiently. Therefore, coding becomes a fundamental component of the AI domain.

Which Language Do Employers Prefer for AI?

There is no one programming language used for Deep Learning and Machine Learning. You can choose from a wide range of options available. However, employers generally prefer that candidates should know Python. Python is the best-suited language for Machine Learning, mainly because it’s easy to use when handling datasets. Python also has a shorter learning curve than most other programming languages. 

What is Python:

Python is among the most popular and widely-used programming languages in AI. Its simplicity, scalability and flexibility contribute significantly to its success. It’s easy to use Python seamlessly, with data structures and AI algorithms used frequently. Additionally, Python is an open-source language, allowing development teams to modify and improve it over time by simplifying its syntax. This contributes to the increased efficiency of Python compared to other programming languages. For instance, for Mevolving, developers generally apply Tensorflow but opt for PyTorch when it comes to NLP (natural language processing). Additionally, Python is better suited for small scripts and easily supports enterprise apps. All these factors and the short learning curve make Python more favourable among AI enterprises and developers. 

Prerequisites for AI Roles:

To land a stable role in the Artificial Intelligence domain, there is a specific knowledge set that you require and a few areas with which you should familiarise yourself. These are:

- Concepts within statistics, computer science, Machine Learning, Neural Networks, Deep Learning and distributed computing

- Programming skills, especially Python, R or Scala

- Database skills and understanding of platforms like Hive, Apache Spark, SQL and No SQL

- Other frameworks and platforms include Hadoop or any related Big Data-implementations such as MS Azure Big Data, AWS Big Data; Apache Spark and Apache Kafka

Artificial Intelligence Career Paths:

when you understand the roles available in the it market today within the ai domain, you can make a more infor med career decision. you will know which areas interest you, which skills are more important and where you need to focus more. take a look at the top artificial intelligence jobs available in today’s market. you can branch off into any of these roles once you complete a formal course or certification in artificial intelligence. 

Machine Learning/AI Developer or Engineer:

The primary responsibilities of a machine learning or AI developer include statistical tests, statistical analysis, the designing and developing of ML programs, development of Deep Learning systems, training ML systems using accurate data sets and implementing and deciding suitable ML/AI algorithms. 

Prerequisites:

- Concepts: Machine Learning, Statistics, Computer Science, Deep Learning, NLP and Cloud Computing

- Programming Skills: Python, Java and Scala

- Frameworks: SciKit, Apache Hadoop, H2O, Azure ML Studio, Apache Signa, Amazon ML and Spark MLib

Data Scientist:

Data scientists are responsible for identifying data sources and streams, working closely with data engineering teams, automating data collection workflows, analysing Big Data to find patterns and trends, building predictive and statistical models for building appropriate ML systems and proposing strategies for stakeholders with compelling data visualisation techniques and tools.

Prerequisites:

- Concepts: Statistics, computer science, Deep Learning, Machine Learning, Neural Networks, Mathematics and NLP

- Tools: SQL, R, Python, SSAS, SAS and Scala

- Frameworks and platforms: Hadoop, Spark MLib, Scikit Learn, Azure ML Studio, H2O and Amazon Machine Learning

Data Science Lead/Analytics Manager:

A data science lead is responsible for leading teams of data scientists, managing resources efficiently and providing directions. This role requires a deep understanding of ML architecture, Big Data systems, data science protocols and techniques and strong interpersonal and project management skills. Analytics managers are responsible for setting priorities for analytics and data science teams and communicating results to the senior management teams. 

Prerequisites:

- Concepts: Data science, business analytics and fundamentals of machine learning

- Tools: Excel, Tableau , Power BI, SQL and Python or R

- Soft skills: Project management and communication skills

Research Scientist:

Research scientists need to have a strong grasp of several disciplines of AI, such as computational statistics, applied mathematics, deep learning and Machine Learning. Research scientists must have advanced doctoral or master’s degrees in computer science, like data scientists. 

Prerequisites:

- Concepts: Statistics, computer science, Machine Learning, mathematics, Deep Learning, reinforcement learning, NLP and neural networks

- Programming skills: Python, Scala, R, SSAS and SAS

- Frameworks: Scikit Learn, Apache Hadoop, Spark MLib, Azure ML Studio, H2O, Apache Singa and Amazon Machine Learning

How to Start a Career in AI with a Non-IT Background:

Not everyone interested in Artificial Intelligence has an IT background, but they can significantly impact the industry. For instance, art backgrounds give you a strong understanding of pattern and behaviour recognition. Combined strategically with the fundamentals of AI can provide you with a competitive edge and future-proof career. Here’s how you can achieve your goal:

1. The first step is to strengthen your programming skills. Start with Python, the most accessible and widely-used programming language. Simple coding exercises can go a long way and help you quickly scale up. To understand programming, you must know several basic computer science concepts first. For instance, you can begin by writing algorithms and code for small programs to get a general understanding of Machine Learning algorithms. 
2. Take the time to learn about database management systems.
3. Working knowledge of mathematics is critical for neural networking, Deep Learning and Machine Learning. Brush up on your mathematics skills with a mix of both fundamental and advanced skills. If you aren’t looking for ML coding, this step is not required. 
4. Master concepts like basic statistics, random variables and probability.
5. Learn as much as you can about Machine Learning algorithms. This includes the objective of Machine Learning, how to achieve machine learning and the various tools and packages that can help with ML programming, Deep Learning and NLP.

How to Start a Career in AI with an IT Background:

If you belong to an IT background, it’s pretty common to have at least some experience with programming, database management and the systems that govern them. This makes it easier for you to venture into the AI domain and start a promising career. 

1. Brush up your programming skills and get R or Python programming experience.
2. Learn how to draw insightful visualisations using data visualisation packages available in R or Python. Then, learn how to build a story around these visualisations.
3. Brush up your skills in Deep Learning, Neural Networks and Machine Learning.
4. Brush up on concepts of Combinatorics, Statistics and Probability, Bayesian statistics and descriptive statistics. 
5. Use Machine Learning with R or Python. Learn as much as you can about Scikit Learn, pandas and Seaborn as well as the Python packages available for Machine Learning and Data Science algorithms.

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