Machine learning is one of the IT industry’s hottest new trends right now, and it’s all set to gain prominence in the future tech industry. With different companies such as Microsoft, Apple, and Google rolling out their own developer tools, the interest and commitment of developers are at an all-time high. Machine learning projects will increase dramatically in the near future, leading to job growth, according to 96 percent of professionals. This means that as long as you have the necessary skills, you can apply for a lucrative machine learning job. The question is, what does “necessary” mean when it comes to cementing a promising career and actually getting a job in learning machine? While the machine learning scope is vast, by upgrading some specific skills — some basic and some not – so – basic, you can find success. Let’s see what it’s like.
Learn the prominent machine learning languages
If you lack knowledge in these languages, you won’t be able to hold on to an ML job. Each serves a particular purpose. C++ is useful when it comes to plots and statistics to speed up any coding work you might have on your plate, while R works wonders. Hadoop is a programming language based on Java, which means that in Java you will need to implement reducers and mappers.
Brush up on your statistics and probability skills
The more theories you learn, the easier algorithms can be understood. But without a thorough understanding of statistics and probability, understanding Hidden Markov models, Naive Bayes, and Gaussian Mixture Models is difficult. You can use statistics for p-values, receiver-operator curves, and confusion matrices in the form of a model evaluation metric.
Possess great programming skills
You need to learn everything you can from the programming world to be in demand for a hot machine learning job. Brush up on your programming and computer science knowledge because you need to be totally comfortable with concepts such as algorithms, data structures, and computer architecture to get a machine learning job. Keep in mind that ML algorithms are not isolated and are predominantly part of larger systems. ML programmers therefore need to work comfortably with APIs to create interfaces that are ready for the future. You will also find it useful to learn about the basics of the life cycle of software development.
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