4 essential skills for engineers to understand AI and Machine Learning
AI and Machine Learning in engineering could be the next big thing — where the technology is used at more advanced levels. We listed four essential skills for aspiring engineers to develop in order to excel in the era of AI and machine learning
AI and machine learning are revolutionising industries. For instance, self-driven cars are no longer a futuristic fantasy, with companies like Tesla and Waymo actively developing and testing autonomous vehicles.
Then there are robots powered by machine learning optimising assembly lines and improving product quality in manufacturing industries. AI is already detecting fraudulent transactions and personalising investment strategies in finance.
Similarly, AI and Machine Learning in engineering could be the next big thing — where the technology is used at more advanced levels. For engineers, to truly harness the power of this technology, a new breed of skilled professionals is needed. This is where you come in.
We listed four essential skills you need to develop to excel in the field of AI and machine learning.
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Building the foundation with technical skills
A firm grasp of languages like Python, Java or C++ could be the initial steps. A strong foundation in mathematics and probability is equally important. Sadia Ferdous Samindra works at BRAC and deals with AI. She explains how probability is useful in helping to understand and build models, evaluate performance and help you work with data.
These are the four essential technical skills required: 1) Understanding data structures, algorithms and software engineering principles; 2) Having a strong base in statistics, and dealing with concepts such as probability, linear algebra and calculus; 3) Data cleaning and Data Modelling Techniques; and 4) Learning a Software Development Tool such as Jupyter Notebook, which can be used for experimentations and visualisations.
Understanding deep learning and neural networks
Deep learning models are used in AI products such as digital assistants, facial recognition and fraud detection. It is one of the main components in devices which require image recognition, such as cars and aircraft with autopilot mode, and natural language processing such as virtual assistants such as Siri and Alexa.
Leveraging your mathematical and programming skills, you have to learn how to navigate deep learning; in other words, you need to know how to prepare a machine to understand and work like a human.
As a subset of machine learning, deep learning uses neural networks with many layers to analyse various kinds of data. Much like human neurons, these neural networks can learn from large amounts of data and provide the desired output.
There are two popular online free courses for understanding 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 issues that require innovative problem-solving skills, particularly related to model performance, data quality and scalability. Strong problem-solving abilities are crucial for quickly addressing these challenges and building effective models.
Samindra highlights the importance of "debugging," the process of identifying and fixing issues in data, code and models. This regular task demands excellent problem-solving skills to resolve unique problems and improve algorithms.
Collaboration with data scientists, analysts and developers is imperative to complete projects successfully. Effective communication aids in presenting and documenting work, facilitating collaboration and future development.
Adaptability and quick learning
Engineers must embrace continuous learning to stay current with new technologies and advancements. This involves reviewing research papers, attending conferences and workshops, and experimenting with cutting-edge technology to enhance skills and remain competitive.
Developing a strong foundation with quality courses on platforms like Coursera, participating in hands-on boot camps, and understanding theoretical basics such as mathematics help engineers navigate AI complexities and drive innovation.
Engaging in coding competitions on sites like Kaggle adds a competitive edge and makes learning enjoyable. The ever-evolving field of AI offers endless opportunities for growth and discovery.
Keep learning and exploring to stay ahead.
