The world has been evolving rapidly with technological advancements. Out of many of these, we have AI (Artificial Intelligence) And ML (Machine learning). From automated cars to android systems in many phones, apps, and other electronic devices, AI and ML have a wide range of impacts on how easy machines and AI can make our lives. Given the pace of industry growth, the demand for AI and ML engineers is also increasing; in turn, increasing the demand for data science courses.
So, what are the essential skills to become an AI or ML engineer? Let us begin by understanding the two concepts first.
Although both AI and ML engineers work under the common umbrella of artificial intelligence, their job roles and responsibilities vary slightly based on the tools and techniques in use, and end-results. Machine Learning chiefly focuses on the accuracy of data, and Artificial Intelligence prioritizes on the chances of succeeding over accuracy. While AI engineers use data for decision-making, ML engineers learn new things from the data. AI engineers use Java Programming, C ++, and other software development tools; while ML engineers are required to know algorithms and data tools like H2O, TensorFlow.
Essentially, these two job roles get the same output using different methods. However, many top companies are hiring professionals skilled in both AI and ML. Listed below are some of the top skills that companies look out for.
A good understanding of programming languages, preferably python, R, Java, C++ is necessary. They are easy to learn, and their applications provide more scope than any other language. Python programming 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 industry.
Competence in understanding Signal Processing and the ability to solve several problems using Signal Processing techniques is crucial for feature extraction, an important aspect 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 solve complex situations.
A solid foundation and expertise in algorithm theory are surely a must. This skill set will enable understanding subjects like Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential Equations, and Summations.
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.
You get to master all these skills while you learn machine learning with Python or tools like R, Tableau, SAS, etc., and work on real-world projects.
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.
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 domain works and what benefits the business is crucial. For example, if you have to apply AI or ML in genetic engineering, then you need to have a good understanding of genetic biology.
Proper domain knowledge also facilitates 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 different techniques to fasten developing a scale model.
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.
As we saw you require technical, statistical, and non-technical skills to execute data science projects with complete success, it is good to learn them as you start your career. KnowledgeHut’s learn machine learning with Python course helps you do that. And as you start working on more complex projects make sure you acquire the kind of certification that fast-tracks your career as well.
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