How to Become an AI Engineer: Build a Career in Artificial Intelligence

Artificial intelligence (AI) is the most revolutionary technology today that is quickly reshaping the world around us. From autonomous vehicles to medical diagnosis to customer support, AI makes life easy by solving tough problems and enhancing our lives in numerous ways. It is one of the most in-demand technical skills recruiters require, and becoming an AI engineer is also very rewarding. In order to assist you on your journey, we have developed this helpful guide that will guide you on how to become an AI engineer.

Step-by-Step Process to Become an AI Engineer

One of the primary reasons individuals don't immediately rush into AI is that they don't know where to begin. Many technicalities get in the way when looking for resources to learn Artificial Intelligence. Here are some suggestions before you get started learning Artificial Intelligence.

Work Upon Fundamentals

One of the best ways to get started in AI is to brush up on the fundamentals. You can begin with elementary math, e.g., computer science basics, statistics, probability, vectors, linear algebra, calculus, etc. 

You can then move on to learning a programming language such as Python or R, as both languages have extensive libraries that can implement complex algorithms with just a few lines of code.

In addition to these fundamentals, there is more knowledge to help you better manage your data. Here, the basic concepts of databases such as SQL, NoSQL, relational and non-relational databases, data visualization tools like Tableau or MS Excel, and data formats (JSON, CSV, XML), among others, stand out.

Enhance Your Mathematics Knowledge

Mathematics and computer programming are fundamentals of AI engineering. Algebra, calculus, and statistics should be understood well. In addition to this, knowledge of programming languages ​​like Python, R, Java, and C++ is required for model development and deployment.

Study Machine Learning and Deep Learning

Machine learning and deep learning form the basis of AI. Study machine learning algorithms and deep learning techniques, such as neural networks, to discover how machines learn and adapt through experience.

Learn Data Pre-processing

Once you understand how to deal with data, it is recommended that you learn different techniques to convert unstructured data into structured data and obtain valuable information by applying Machine Learning algorithms. These methods include principal component analysis, dimensionality reduction, normalization, data elimination, sampling, feature extraction, etc.

Engage in Hands-on Projects

Theory is critical, but practice does it. Engaging in practical projects gives you the experience that you require to apply your knowledge in real-world situations. Think about creating machine learning models that will be able to solve certain problems or improve current procedures. 

Build a Portfolio

Having a strong portfolio is a great way to demonstrate your capability as an AI engineer. Emphasise projects you completed, noting the problems you solved and how you implemented AI solutions. Having a strong portfolio can be a job-seeker's best friend when looking for job opportunities. So how do you do it?

• Create freelance AI projects: Develop freelance projects to demonstrate your expertise. This can include anything from building a chatbot to forecasting trends in data sets..
• Contribute to open-source projects: Participate in AI open-source projects to contribute to the community and learn from other developers.
• Engage in AI competitions: AI competitions are a great place to gauge your skills and establish your worth. Data science and AI competitive platforms, such as Kaggle, DrivenData, Numerai, and CodaLab, offer a series of exciting challenges along with opportunities to solve real-world problems and acquire new skills.
  

Keep up to date

AI is a field that is always changing. Stay up-to-date on the latest research, developments, and trends by attending conferences, workshops, AI application courses, and refresher courses. Additionally, read blogs and scientific journals to stay informed about emerging trends and best practices in the sector.

Since AI is constantly changing, you need to stay informed about the latest developments through continuing research and education.

Attend Workshops and Conferences

Participate in AI workshops and conferences to network with professionals and stay up-to-date.

Become a member of online forums and AI communities: Join online communities and forums to share ideas, raise questions, and form meaningful connections.

Best Resources to Learn Artificial Intelligence

Now that you have worked on your basics and figured that you are all set to enter the technicalities of Artificial Intelligence, you need to explore different types of resources available to help you learn the subject. Today, there are a lot of online courses and tutorials, YouTube channels, distance learning courses, bootcamps, and MOOCs (Massive Open Online Courses) from recognized universities and institutes, which follow tested and proven methodologies to help you learn the subject.

After completing basic training, you can always start a specialization in a specific domain, such as big data, robotics, deep learning, computer vision, machine learning, cognitive robotics, computational perception, and natural language processing (NLP), among others.

Online Artificial Intelligence Courses

Below are some online learning resources from the leading online learning platforms.

• Welcome to Artificial Intelligence by Udemy: The course can help you understand the basics of Artificial Intelligence and Machine Learning.
• AI for Everyone by IBM on Coursera: AI for Everyone is among the most popular AI courses online. It covers the basics of AI, including neural networks, AI ethics, problem-solving in AI, building AI strategies, etc.
• Knowledge-Based AI: Cognitive Systems by Udacity: This is a free, advanced-level AI course that covers structured knowledge representations, problem-solving methodologies, planning, decision-making, and learning methods.
• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning by Coursera: As the name suggests, you will get to work with TensorFlow to implement the principles of Artificial Intelligence, Machine Learning, and Deep Learning in creating scalable models WRT real-world problems.
• IBM Applied AI Professional Certificate by IBM on Coursera: The IBM Applied AI Professional Certificate will give you an understanding of AI technology, its applications, and its use cases.
• Fundamentals Of Artificial Intelligence by NPTEL: Fundamentals of Artificial Intelligence by IIT Guwahati on NPTEL gives an overview of concepts of problem-solving, knowledge representation, reasoning, and learning methods of AI.

Books on Artificial Intelligence

Books never go out of fashion when it comes to learning content thoroughly and keeping knowledge handy. These artificial intelligence books can help you further sharpen your skills.

• Artificial Intelligence: A Modern Approach: by Stuart Russell & Peter Norvig: This is a series of three books that cover the basic concepts of AI and are particularly helpful for beginners or non-programmers.
• Driven: The Race to Create the Autonomous Car by Alex Davies: The book refutes the outdated theory of big, brawny, brainy dudes reshaping the future and talks about autonomous vehicles or, popularly, robot cars.
• New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale: The book explores the world of AI, offering a glimpse into the future where factory robots, medical diagnosis algorithms, and online learning are prevalent.
• Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain by Amit Konar: This book focuses on the tools and techniques required for the intelligent realization of machines.
• Human + Machine by Paul R. Daugherty and H. James Wilson: This book explores the role of artificial intelligence in business and economic applications.

YouTube Channels to Learn AI

YouTube channels are among the most popular learning resources among tech enthusiasts. All you have to do is get subscribed to your chosen channel and learn the practical aspects of artificial intelligence or other related topics. Let’s look at some of the popular YouTube channels for AI learners.

• 3Blue1Brown: Explains the content through animation.
• Computerphile: Computerphile primarily focuses on AI techniques, covering a range of content that includes security vulnerabilities, processing power, and cybersecurity.
• Two Minute Papers: With over 1.68 million subscribers, this channel is a hit among hardcore learners. Two Minute Papers shares summaries of newly published AI research.
• Artificial Intelligence: All in One: This channel covers content related to Artificial Intelligence, Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, and Data Science.

More Resources to Learn AI

Apart from the course format, we can and should use much more content on the Internet to deepen our knowledge about Artificial Intelligence. Let us review some of them:

• Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data: The Becoming Human blog offers this huge compilation of cheat sheets on AI and related topics
• Machine Learning Glossary: A comprehensive glossary with graphics and formulas on concepts related to machine learning.
• Mining of Massive Datasets: A book on big data, written by three Stanford professors, and legally available on the Internet
• Understanding Machine Learning: From Theory to Algorithms: Book is used as the basis of many university courses on machine learning and is legally available on the Internet. In English.
• A Brief Introduction to Neural Networks: Book posted by its author, a German data scientist, to introduce us to neural networks
• Notes on Using Data Science & Machine Learning to Fight for Things That Matter: The director of machine learning at the Wikimedia Foundation offers an extensive compilation of commented code cheats.

Here are some additional tips for getting started with AI:

• Focus on the basics. Take your time, try to learn slowly and grasp concepts better. Focus on learning the fundamental concepts of AI, such as linear algebra, calculus, probability theory, statistics, and computer science.
• Be patient. Learning AI takes time and effort. Don't get discouraged if you don't understand everything right away. Just keep practising and learning.
• Find a mentor. If you know someone experienced in AI, ask them to be your mentor. They can provide guidance and support as you learn.
• Join a community. There are many online and offline communities for AI enthusiasts. Joining a community can help you stay motivated and learn from others.

Artificial Intelligence Engineer Salary

As per AmbitionBox, the average artificial intelligence engineer salary is INR 17.8 LPA, with an average experience of 0 - 6 years.

Conclusion

This new world, full of intelligent systems capable of learning quickly, brings us many benefits and presents us with enormous challenges. In this reality, where AI is gaining prominence daily, we can either be consumers or take it one step further. Discover the concepts of AI through these top resources to make a career in artificial intelligence and become an AI engineer.