“Future Artificial Intelligence (AI) is more about human emotions, compassion, values and elimination of human sufferings.” – Amit Ray
The world is constantly evolving with many technological developments in both Machine Learning (ML) and Artificial Intelligence (AI). These have an impressive impact on several machines from the android system in phones and other various electronic devices to automated cars, the impact of ML and AI is growing tremendously, it is estimated that ML and AI will offer more than 2 million job opportunities within the next couple of years.
The AI revolution has given rise to many new disruptions one such disruption is ML, which is a subfield of artificial intelligence, which enables machines like a computer system to make predictions or take decisions from past data or experiences without being explicitly programmed. ML uses a huge amount of structured and semi-structured data so that the ML model can generate appropriate results or allow predictions depending on the data.
Deep Learning is also called deep neural learning or deep neural network. It is a subfield or part of ML, in which the algorithms are built and function that are inspirations or replications of the human brain, developed to imitate the structure and work of the human mind, and are called Artificial Neural Networks(ANN).
In this article, let’s understand why an individual should learn about ML and deep learning, how they can master in these fields, the various job roles that are available, which industries and firms are hiring professionals.
ML and Deep learning are booming fields, which offer exciting career opportunities for qualified and skilled professionals. Many big tech enterprises, but also medium and small enterprises are embracing AI and machine learning, and have adopted its usage for giving better services and products, from the healthcare industry to aviation, to security and manufacturing. ML jobs are projected to be worth almost US$31 billion by 2024. That’s an annual growth rate of more than 40% over a six-year period
This resulted in the huge rise of the job market, and the demand for AI and ML professionals was increased.
AI will be offering about 133 million new jobs by 2022
- The Enterprisers Project
40 percent of the respondent organizations are offering more jobs as a result of using artificial intelligence technology.
- Dun & Bradstreet
Individuals who want to step into these fields are required to have good knowledge of certain skills, which they can get by doing a relevant certification program that gives them practical exposure to the various algorithms and it’s usage in the real-life. A better understanding of various tools and techniques will make their career in artificial intelligence a smooth ride.
To get into these fields the most important prerequisites is a strong technical background, as these are highly technical fields. Having good knowledge of ML and AI skills with an advanced degree is something many industries will look for before hiring a candidate. The education prerequisite to enter this field is, one should have a bachelor’s degree either in computer science or mathematics or information technology or statistics or finance or economics.
There are many career opportunities in AI, ML, and Deep Learning. Skills play a vital role and provide clear knowledge of the higher-level research to low-level programming and implementation. Good knowledge of both technical and non-technical skills will be helpful for an individual to have a rewarding career. One can enhance their skills by doing a certification.
To keep up with the latest advancements in the tech industry it is essential to learn programming languages. There are several programming languages for AI, and it is very difficult to say which programming language is the best. The most prominent programming languages for AI are as follows:
Python is the most popular programming language which is used in ML. It is platform-independent and collaborates easily with other AI programming languages.
Good knowledge of algorithm theory and knowing how the algorithm functions are very important. Topics like gradient descent, lagrange, convex optimization, partial differential equation, quadratic programming, and summations are very essential. Additionally, one should understand deep learning algorithms and the ways they can be implemented using a framework.
Statistics is the process of analyzing the dataset to identify the unique mathematical aspects. ML starts as statistics and then advances further. Mean, median, mode, variance, average, and standard deviation are helpful to describe the dataset. An individual must be familiar with matrices, vectors, and matrix multiplication. A firm knowledge of probability will help them to understand several models ML and AI models.
NLP is about combining computer science, information engineering, linguistics, and AI into one, and programming the system to process and analyze huge datasets. The various NLP libraries are:
Natural Language Toolkit (NTLK)
A neural network is a system (software or hardware) that works like a human brain. Based on the neural functionality of the human brain, the concept of artificial neural networks is developed. It not just replicates the human understanding, but also leverages tasks that are far beyond the capabilities of humans.
RPA is the application of technology that gives the option to customize computer software or a “robot” to capture and interpret the existing applications for processing a transaction, triggering responses, manipulating data, and communicating with other digital systems.
The most successful AI projects are those that approach the real pain points effectively. It is important to have a good knowledge of the industry in which one is working and the ways one can benefit the business to grow.
Individuals should stay updated with the latest industrial trends and data so that they can develop better outputs based on the findings. They should adopt the best business practices and new approaches to AI.
Iterating on ideas is very essential for finding one that works. In ML, this is applicable to everything from the selection of the right model, to working on projects, which include A/B testing, NLP libraries. One must use a variety of techniques to quickly fabricate realistic scale models of solid parts or assemblies using 3D computer-aided designs, mainly when working 3D models.
ML and Deep learning tools are AI-algorithmic applications that offer systems with the ability to understand and enhance without considerable human input. It ensures software, without being explicitly programmed, predicts results more accurately. A good understanding of these tools will help you improve your hardcore technical knowledge. The details of a few tools are as follows:
Keras is a deep-learning Python library that can run on top of Theano, Tensor Flow. It runs on CPU as well as GPU. It is very possible. It is more popular because of the user interface, ease of extensibility, and modularity.
Scikit-learn is an open-source library for ML. Python is a scripting language of this framework and includes various models of ML which includes classification, regression, clustering, and reduction of dimensionality. Scikit-learn is developed on three open-source projects, which are Matplotlib, NumPy, and SciPy.
Amazon SageMaker supports open-source web applications. It offers data researchers and developers to develop, train and implement ML models on any scale very easily and quickly. It is very helpful in storing code in volumes, which are protected and encrypted by security groups.
There are different and multiple career paths in the areas of ML and Deep Learning. The average salaries are also having big figures. It can be clearly said that an individual who wants to enter into these fields will have an exciting and bright career. People who have industry-required skill sets will be in higher demand.
World Economic Forum(WEF) had forecasted a tremendous increase in AI jobs globally over the next two years, with new jobs per 10,000 opportunities increasing from 78 today to 123 in 2022
In the area of ML and Deep learning, various roles are available. Here is a list of a few relevant career options that are in high demand and their average salaries:
AI engineer – Are problem-solvers who built, test, and apply various models of AI and also effectively handle AI infrastructure. They take the help of ML algorithms and neural networks to develop better AI models.
Average salary per annum - US$116,540(Glassdoor)
ML engineer - They develop and maintain self-running software which facilitates machine learning initiatives. They work with large amounts of data and possess extraordinary data management traits.
Average salary per annum - US$121,106(Glassdoor)
Business Intelligence (BI) developer – They develop, model, analyze, and also maintain complex data. They consider the business acumen along with AI to enhance the business revenue.
Average salary per annum -US$90,430 (Glassdoor)
Robotic Scientist – They efficiently boost the tasks done by the robots. Their demand is quite more in several major industries for programming their machines to develop mechanical devices or robots, which can perform tasks with commands from humans.
Average salary per annum -US$83,241 (Glassdoor)
Skilled individuals are needed in almost every sector, not just technology. A few of the top industries are:
Technology and home automation
Government and military
The job opportunities in ML and Deep Learning are only going to grow as the technology continues to advance. There are many top firms who are hiring skilled professionals. A few of them are listed below:
The high-paying job opportunities in these areas are expected to grow more in the coming years across many industries. If an individual is planning to pursue a career in ML and Deep Learning, then make sure that they have the required skillset and certifications. By doing certifications one can enhance their chances to get into a better job in a well-established company.
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