The world has been evolving rapidly with technological advancements. Out of many of these, we have AI (Artificial Intelligence) and ML (Machine learning). The era of machines and robots are taking center stage and soon there will be a time when AI and ML will be an integral part of our lives. From automated cars to android systems in many phones, apps, and other electronic devices, AI and ML have a wide range of impact on how easy machines and AI can make our lives. The future of these technologies is quite promising; it is beyond our wildest imagination. So, there is already and will be a lot of demand for AI and ML professionals, known as AI and ML engineers. Before understanding the essential skills required to become an AI and ML engineer, we should understand what kind of job roles these two are.
Although they look the same, there are some subtle differences between AI and ML engineers. It boils down to the way they work and the software and languages they work on, to reach one common goal: Artificial Intelligence. Simply put, an AI engineer applies AI algorithms to solve real-life problems and building software. On similar terms, an ML engineer utilizes machine learning techniques in solving real-life problems and to build software. They enable computers to self-learn by giving them the thinking capability of humans. Like mentioned earlier, these two job roles get the same output using different methods. However, many top companies are hiring professionals skilled in working both on AI and ML.
The capability of an astounding AI and ML engineer is reflected by both the technical and non-technical skills. Let us see what it takes to be one of these two professionals.
A good understanding of programming languages, preferably python, R, Java, Python, C++ is necessary. They are easy to learn, and their applications provide more scope than any other language. Python is the undisputed lingua franca of Machine Learning.
It is recommended to have a good understanding of the concepts of Matrices, Vectors, and Matrix Multiplication. Moreover, knowledge in Derivatives and Integrals and their applications is essential to even understand simple concepts like gradient descent.
Whereas statistical concepts like Mean, Standard Deviations, and Gaussian Distributions along with probability theory for algorithms like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models are necessary to thrive in the world of Artificial Intelligence and Machine Learning.
A Machine Learning engineer should be competent in understanding Signal Processing and able to solve several problems using Signal Processing techniques because feature extraction is one of the most critical aspects of Machine Learning. Then we have Time-frequency Analysis and Advanced Signal Processing Algorithms like Wavelets, Shearlets, Curvelets, and Bandlets. A profound theoretical and practical knowledge of these will help you to solve complex situations.
A solid foundation and expertise in algorithm theory is surely a must. This skill set will enable understanding subjects like Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential equation, and Summations.
As tough as it may seem, Machine Learning and Artificial Intelligence are much more dependable on mathematics than how things are in, e.g. front-end development.
Machine Learning is used for complex tasks that are beyond human capability to code. Neural networks have been understood and proven to be by far the most precise way of countering many problems like Translation, Speech Recognition, and Image Classification, playing a pivotal role in the AI department.
Communication is the key in any line of work, AI/ML engineering is no exception. Explaining AI and ML concepts to even to a layman is only possible by communicating fluently and clearly. An AI and ML engineer does not work alone. Projects will involve working alongside a team of engineers and non-technical teams like the Marketing or Sales departments. So a good form of communication will help to translate the technical findings to the non-technical teams. Communication does not only mean speaking efficiently and clearly.
Machine learning projects that focus on major troubling issues are the ones that finish without any flaws. Irrespective of the industry an AI and ML engineer works for, profound knowledge of how the industry works and what benefits the business is the key ingredient to having a successful AI and ML career.
Channeling all the technical skills productively is only possible when an AI and ML engineer possesses sound business expertise of the crucial aspects required to make a successful business model. Proper industry knowledge also facilitates in interpreting potential challenges and enabling the continual running of the business.
It is quite critical to keep working on the perfect idea with the minimum time consumed. Especially in Machine Learning, choosing the right model along with working on projects like A/B testing holds the key to a project’s success. Rapid Prototyping helps in forming an array of techniques to fasten building a scale model of a physical part. This is also true while assembling with three-dimensional computer-aided design, more so while working with 3D models
With Natural Language Processing, AI and ML engineers get the chance to work with two of the foremost areas of work: Linguistics and Computer Science like text, audio, or video. An AI and ML engineer should be well versed with libraries like Gensim, NLTK, and techniques like word2vec, Sentimental Analysis, and Summarization
Physics: There will be real-world scenarios that require the application of machine learning techniques to systems, and that is when the knowledge of Physics comes into play.
Reinforcement Learning: The year, 2017 witnessed Reinforcement Learning as the primary reason behind improving deep learning and artificial intelligence to a great extent. This will act as a helping hand to pave the way into the field of robotics, self-driving cars, or other lines of work in AI.
Computer Vision: Computer Vision (CV) and Machine Learning are the two major computer science branches that can separately work and control very complex systems, systems that rely exclusively on CV and ML algorithms but can bring more output when the two work in tandem.
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