Introduction
The rise of Artificial Intelligence (AI) has been met with a mixture of excitement and anxiety. Many experts see it as a revolutionary technology that will transform our world. Others worry that AI could usher in an age of unprecedented unemployment. It could even lead to the creation of machines with superhuman intelligence and thus outsmart the human brain.
However, one thing is clear: the demand for skilled AI professionals is growing rapidly. Intelligent machines are a tremendous help in solving complex problems for tomorrow. With the right skills and training, a career in AI can be gratifying and fulfilling.
This blog post will serve as a guide to starting a career in artificial intelligence and machine learning. We’ll cover some of the key skills and educational paths that you need to get started, as well as resources for finding jobs in the field.
Table of contents
Also Read: Why AI is the Next High Paying Skill to Learn.
Ready To Start Your Journey?
Let’s take a moment before diving into the specifics of how to break into the world of AI. It’s important to understand what the artificial intelligence career path entails. At its core, a career in machine learning or artificial intelligence involves anything related to building intelligent algorithms.
Artificial intelligence jobs range from developing machine learning algorithms for self-driving cars to performing data analysis for healthcare startups. In other words, AI professionals work at the cutting edge of technology and are responsible for shaping the future of our world. The average salary for professionals in this field is relatively high, with AI specialists earning over $100,000 on average.
Here are the most common job titles in the field:
Machine Learning Engineer
Machine learning engineers are responsible for designing and building machine learning systems. They use programming languages and tools like Python, MATLAB, or R to build models that can learn from data and predict future events.
Machine learning engineers typically have a computer science degree or background in mathematics. They also have an interest in solving complex problems with the help of algorithms.
Engineers with a machine learning focus typically work at tech companies, but there is a growing demand for them in other industries.
Business Intelligence Developer
In the business world, businesses need to make decisions based on data. Business intelligence developers use machine learning tools like SAS, SQL, and Excel. They analyze large datasets for insights that can help inform crucial business decisions.
They’re responsible for data visualization, predictive modeling, and business analytics. A background in business and data analysis is essential for this role, as well as programming experience with languages like R or Python.
Many business developers work at large corporations that need to optimize their performance. However, there’s also a growing demand for them in the startup world.
Software Engineers
Since AI is fairly complex for laypeople, most AI systems are packaged and delivered as software products. As a result, software engineers play an important role in developing AI products.
These workers take the machine learning algorithms developed by engineers and use them to create machine learning software. They may also be responsible for writing the core programming code. They’ll test the performance of AI systems or optimize machine learning algorithms for speed and efficiency.
Data Scientist
Business analysts and engineers can produce massive data from day-to-day operations. Data scientists are responsible for analyzing and extracting useful insights from this data. Of course, AI does a lot of the legwork for data scientists. Algorithms can automatically extract and organize important information, such as trends or patterns.
But like software engineers, data scientists play an essential role in developing AI systems. They might help to optimize machine learning models or create new ones from scratch. A background in statistics is usually required for this role, along with a strong understanding of machine learning and programming languages.
Big data architects are also in high demand for jobs related to artificial intelligence.
Researcher
You might consider becoming a researcher if you want to focus on real-world problems. Think of healthcare or environmental issues. Researchers work at universities, labs, and companies to investigate applications of AI in various industries.
They may work on projects related to natural language processing, image recognition, or computer vision. Most researchers have a Ph.D. in artificial intelligence or machine learning. They also have several years of experience working with these technologies.
Getting an AI Education: Skills Required
Before you can leverage artificial intelligence in a successful career, you’ll need to prove your human intelligence by acquiring the necessary education. In most cases, you’ll need a graduate, master’s, or doctoral degree in artificial intelligence or a related field. Related fields include computer science or data science.
These degrees are not only a requirement for many artificial intelligence jobs. They will also help you gain the technical, analytical, and problem-solving skills needed to succeed in this field. Essential skills like critical thinking are also crucial in the artificial intelligence field. Remember that you won’t always speak in human language when working with an AI system. You’ll need to be able to communicate effectively using code and other technical tools.
The skills gap in the AI field continues to grow, so you must stay up to date with new developments in this area. You can do this by attending conferences or webinars. Reading industry publications and participating in online forums can also help.
As for business knowledge, you’ll need a few non-technical skills to succeed in your career as an AI professional. Excellent communication skills, for example, are essential when partnering with colleagues. Other soft skills that can be valuable in the artificial intelligence field include problem-solving, time management, and attention to detail.
With these skill sets, your education should lead you to a successful career in artificial intelligence. Of course, there are no guarantees in life, but your hard work will certainly pay off if you’re driven and passionate about this field.
Artificial Intelligence Courses and Curriculum
So, where should you start? From a postgraduate degree to a college degree or online training, several options are available to you. Also, remember that different career paths may require additional degrees or skill sets. Some may require a deeper understanding of programming language. Others may require a degree in mathematics or business.
That being said, you have to start somewhere. You can learn the basics of machine learning in many courses, such as:
Google AI
As one of the leading developers of artificial intelligence technologies, it’s no surprise that Google is also a major player in providing education for AI. The company offers multiple courses on AI topics through its free online learning platform called “Google AI.” You’ll receive a certification or a specialization when you complete the courses.
It’s more geared towards complete beginners, with the exception of TensorFlow. TensorFlow is Google’s open-source machine learning framework, so you’ll need background knowledge in AI to work with it.
MIT
The Massachusetts Institute of Technology is famous for its Professional Certificate Program in Machine Learning and Artificial Intelligence. This online program is designed for people with a computer science or related background. It consists of a 12-week online course that is completed at your own pace.
You’ll learn about application domains, basic systems and algorithms, predictive models, and more.
IBM Skills Network
You might have heard of IBM before, and you may already be familiar with the company’s artificial intelligence platform, Watson. The IBM Skills Network offers a variety of online courses on Watson and AI that can help you develop your technical skills in this area. You’ll learn Python, how to build a chatbot and more.
Stanford University
Few advanced degree programs in this area are as highly respected as the ones offered by Stanford University. The program focuses on systems that learn from data generated in real-time, and you’ll learn about the latest research in this field. You’ll also get familiar with MATLAB and Python, two programming languages that are commonly used in the field of AI.
Nvidia
Known for its graphics processing units (GPUs), Nvidia also offers online courses in areas such as visual computing. If you’re interested in pursuing a career in computer vision, this might be a good option. The Fundamentals of Deep Learning for Computer Vision course consists of online lectures and practical projects.
Sample Core Subjects in an AI Curriculum
There is no set curriculum for education in artificial intelligence, as the field encompasses a wide range of topics and skill sets. However, some core subjects are typically included in all AI programs. These subjects may include, but are not limited to:
Robotics and Automation
Robotics and automation are essential skills for anyone in the field of artificial intelligence. You’ll learn about robot motion, sensors, control systems, and navigation. It is especially useful to have a background in robotics if you are interested in pursuing a career as a roboticist or engineer.
Also Read: Robotics For Teens – Starter Guide.
Mathematics & Statistics
AI is based on mathematics and statistics. You’ll need to have a good understanding of numbers and to problem-solve to work in this field. You might study topics like probability, calculus, linear, or algebra. There’s also graph theory, optimization techniques, and much more.
IoT
As we move towards a more connected world, AI plays an increasingly important role in the Internet of Things. You’ll learn about embedded systems for devices, distributed computing, and sensor networks. Machine-to-machine communication and cloud platforms will also be explored.
Deep Learning
Deep learning is one of artificial intelligence research’s most exciting areas. It’s also one of the most rapidly evolving. There’s image classification and facial recognition, natural language processing, and voice recognition. Deep learning techniques are revolutionizing every aspect of our lives. If you’re interested in pursuing a career in this field, you’ll need to understand the fundamentals of deep learning algorithms.
Neural Networks
Machine learning often takes complex data sets and turns them into understandable patterns. Neural networks work by simulating how neurons in the brain process information. They are an essential component of machine learning systems. Your curriculum may include coursework on supervised and unsupervised learning, backpropagation, and more.
Computer Science
Of course, you must have a solid computer science foundation to pursue a career in artificial intelligence. You’ll learn about underlying programming concepts. Algorithms, data structures, and software design are all in the curriculum. Your studies may also cover programming languages like Python, Java, and C++.
Also Read: Is robotics computer science or engineering?
Recurrent Neural Network (RNN)
In some cases, data can take the form of natural language text or audio. Recurrent neural networks are used to process this kind of data, and they are an advanced topic in the field of artificial intelligence. Not only will you learn how to design and implement RNNs, but you’ll also explore applications of this technology. It’s widespread in areas like sentiment analysis and speech recognition.
Reinforcement Learning (RL)
Reinforcement learning is another exciting area of AI research that may be included in your curriculum. This approach allows machines to learn from experience. It has been used in applications like self-driving cars and intelligent robotics. As you advance through your studies, you’ll become familiar with the fundamental models used in reinforcement learning systems.
Supporting AI Technologies
The complex algorithms that make up artificial intelligence require many different technologies to function. Some of the other topics you may study as part of your AI program include
Cognitive Science
Cognitive science is the study of how we process information. This interdisciplinary field combines psychology, neuroscience, and linguistics. It helps us understand the inner workings of our minds. Understanding cognitive science will give you an edge when working in artificial intelligence.
Natural Language Processing
Natural language processing, or NLP, studies how we interact with and process language. Understanding how people perceive and use language will help you design better AI systems. They will be able to interact more intelligently with humans. We already have AIs that pass the Turing test and interact in natural language, but there is still much work to be done in this field.
Machine Vision and Image Processing
Another key area of AI research is machine vision, which involves using computers to process images and other visual stimuli. Image processing techniques may be used in everything from self-driving cars to medical image analysis. Your coursework will likely include camera calibration, feature extraction, LiDAR scanning, and more.
Distributed computing
Another key area of technology that is important for AI applications is distributed computing. With this approach, large computational tasks are broken down into smaller parts. They are distributed across multiple networked computers to be processed in parallel. This technique can significantly speed up the processing time for certain types of data-intensive jobs. It’s a vital tool for researchers and engineers in artificial intelligence.
Top 10 Books About AI To Read:
Artificial Intelligence – The Basics
Author: Kevin Warwick
Machine Learning for absolute beginners
Author: Oliver Theobald
The Hundred-Page Machine Learning Book
Author: Andriy Burkov
The AI first company
Author: Ash Fontana
Atlas of AI
Author: Kate Crawford
Scary Smart; Solve for Happy
Author: Mo Gawdat
Human Compatible.
Author: Stuart Russell
Game Changer
Author: Matthew Sadler and Natasha Regan
Possible Minds: Twenty-Five Ways of Looking at AI
Author: John Brockman
Working with AI
Author: Thomas Davenport and Steven Miller
This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work.
Responsibilities of Artificial Intelligence Engineer
You will be responsible for designing, developing, and testing AI systems. Some of your specific job duties may include selecting and integrating AI software tools and frameworks. You may also be tasked with conducting performance tests to measure the efficacy of different systems. You’ll analyze experiments’ data to optimize performance. You might also write technical documentation for system users or other stakeholders.
A machine learning project is a complex and rewarding undertaking. It requires a high level of technical skill, creativity, and attention to detail. As an AI engineer, your job won’t be limited to working on the machine learning model. You’ll also be responsible for the overall design of the system and its implementation. Managing the process of testing and refining your model to achieve optimal results also falls under this umbrella.
Your job duties may include selecting and integrating machine learning functions and software tools.
The Skills Required
Once you land a job as an artificial intelligence engineer, you’ll need to be able to demonstrate on-demand skills like:
Developer Skill
To be a successful artificial intelligence engineer, you’ll need strong programming skills, ideally with experience in multiple languages. You should also be familiar with standard machine learning frameworks and libraries. These technical skills will allow you to develop high-quality AI systems that function as designed.
Analytical skill
AI systems rely heavily on data, which can come in various formats and from various sources. For example, Advanced Signal Processing Techniques for ECG Applications datasets may include medical images, sensor data, or audio files. As an AI engineer, you’ll need to be able to manage large amounts of data and use it effectively for machine learning models. This requires strong analytical skills and thinking critically about complex problems.
Business Skill
Depending on the context, your AI system may work directly with humans. It’s vital to have strong interpersonal and communication skills to design systems that effectively interact with people.
Experiential Learning
A lot of the skills you’ll need to be an effective artificial intelligence engineer involve hands-on experience in working with data and developing machine learning models from scratch. Therefore, it’s crucial to have strong project management skills to navigate real-world AI projects.
Mathematical Skill
Translating a computing skill into a mathematical one is an important part of the work you’ll be doing as an AI engineer. You will need a strong understanding of statistics and probability. This includes advanced modeling techniques like linear regression and multivariate testing.
Making a Career in AI
Laying the foundation in your Artificial Intelligence career involves a solid education. However, the field of machine learning is constantly evolving, so it would be best to take the following steps:
Step 1: Break any mindset barriers.
Innovation centricity and entrepreneurship are the hallmarks of a successful artificial intelligence career. Therefore, it’s important to be open-minded, flexible, and willing to take risks to achieve your goals. Business trends shift quickly in AI, so you’ll need to be comfortable with uncertainty and change.
For example, deep learning for computer vision applications is one of the most popular areas in AI right now. But it’s also essential to stay up-to-date on emerging trends and new technologies. In this case, keep an eye on generative adversarial networks (GANs) to stay ahead of the competition.
Step 2: Have a Process.
Deep learning frameworks, such as Tensorflow, are widely used in artificial intelligence. As you develop your skills as an AI engineer, you need a straightforward process for implementing them into your workflow. This might include creating a project roadmap or developing internal best practices for data management.
Step 3: Pick a programming tool.
Many deep-learning engineers prefer to use Python for their development projects. It’s a highly versatile and powerful language with many advanced libraries. It can be helpful when you need to write complex mathematical computations or perform large-scale data analysis.
However, if you are more comfortable using another programming language, you should make sure that you are proficient in it before you start working on an AI project.
Step 4: Practice your skills
The complex algorithms and large datasets used in artificial intelligence can be daunting at first. To develop your skills as an AI engineer, it’s important to practice often and engage with the larger developer community online. That can include attending meetups or participating in online forums and discussions on sites like Reddit or Github.
Additionally, you may also want to attend training boot camps. AI-focused conferences can help you stay up-to-date on the latest trends and developments in the field.
Possibilities with AI
The AI career landscape is full of opportunity and growth. Supervised learning, unsupervised learning, and reinforcement learning are just a few of the many subfields within artificial intelligence that offer exciting career prospects. As you can see, the job opportunities for software developers are vast, and the demand for talented AI engineers is only going to continue to grow.
Machine learning engineers and artificial intelligence professionals can bring their services to many business applications. In a financial institution, fraud detection, risk management, and algorithmic trading are already in use.
In healthcare, machine learning algorithms are often used for predictive analysis in areas like drug discovery. And in e-commerce industries, many companies use these technologies for advertising optimization.
Conclusion
The next Andrew Ng might be out there somewhere. If you’re looking for ways to join the business leaders of artificial intelligence and machine learning, follow these steps and start building your career today! With the right combination of creativity and technical ability, you can build a successful career in artificial intelligence.
