AI and machine learning in engineering could be the next big trend in more advanced uses of technology. Here are four essential skills that aspiring engineers should have to excel in the age of AI and machine learning.
AI and machine learning are revolutionizing industries. For example, self-driving cars are no longer a futuristic fantasy, with companies like Tesla and Waymo actively developing and testing autonomous vehicles.
Additionally, robots powered by machine learning are optimizing manufacturing assembly lines and improving product quality, and AI is already in finance to detect fraudulent transactions and personalize investment strategies.
Similarly, AI and machine learning in engineering could be the next big trend as technology is used at a more advanced level. For engineers, to truly harness the power of this technology, a new type of skilled professional is needed. That's where you come in.
We've listed four essential skills you must have to excel in the field of AI and machine learning.
Build a foundation with technical skills
A solid understanding of languages like Python, Java, and C++ is the first step. A strong foundation in mathematics and probability is equally important. Sadia Ferdous Samindra works at BRAC and focuses on AI. She explains how probability helps understand and build models, evaluate performance, and process data.
There are four essential technical skills required: 1) an understanding of data structures, algorithms, and software engineering principles; 2) a strong foundation in statistics, working with concepts such as probability, linear algebra, and calculus; 3) data cleaning and data modeling techniques; and 4) mastery of software development tools such as Jupyter Notebook that can be used for experimentation and visualization.
Understanding Deep Learning and Neural Networks
Deep learning models are used in AI products such as digital assistants, facial recognition, fraud detection, etc. It is one of the key components in devices that require image recognition such as cars and aircraft with autopilot mode, or natural language processing such as virtual assistants like Siri and Alexa.
We need to learn how to leverage our math and programming skills to advance deep learning — in other words, how to prepare machines to understand and act like humans.
Deep learning, a subset of machine learning, uses neural networks with many layers to analyze different types of data. Similar to human neurons, these neural networks can learn from large amounts of data and provide a desired output.
There are two popular free online courses to understand deep learning: “Deep Learning Specialization” by Andrew Ng on Coursera and “Practical Deep Learning for Coders” by fast.ai.
Communication and problem-solving skills
AI and machine learning engineers face complex problems that require innovative problem-solving skills, especially related to model performance, data quality, and scalability. Strong problem-solving abilities are essential to address these challenges quickly and build effective models.
Samindra emphasizes the importance of “debugging,” the process of identifying and fixing issues with data, code, and models. This recurring task demands strong problem-solving skills to solve unique problems and improve algorithms.
Collaboration between data scientists, analysts, and developers is essential to the success of any project. Effective communication helps present and document work, and fosters collaboration and future development.
Adaptability and quick learning
Engineers must engage in continuous learning to keep up with new technologies and advancements. This includes reviewing research papers, attending conferences and workshops, and experimenting with cutting-edge technologies to advance their skills and stay competitive.
Building a strong foundation through quality courses on platforms like Coursera, participating in hands-on bootcamps, and understanding theoretical fundamentals like mathematics can help engineers navigate the complexities of AI and drive innovation.
Participating in coding competitions on sites like Kaggle can make learning fun and competitive, and the ever-evolving field of AI offers endless opportunities for growth and discovery.
Keep learning and exploring to stay ahead.
