Machine Learning has seen huge advancements in recent years and looks to be holding the future in its pocket.
For those who are not familiar with machine learning, it is essentially the process through which machines take inputs of data, analyze patterns that emerge out of that data and take actions accordingly. This means that machines learn how to behave without a specific code telling them that.
Industries of all sorts employ machines more and more because they are efficient and cost-effective. For example, what you see on your feed when you go online is the result of machines learning about what you like or dislike. They see patterns in your actions and are able to feed you ads and info that might be of interest to you. Facebook uses that, Amazon and any other huge retailer or business.
With increasing, growth comes the natural job market bloom. A new technology or industry that breaks ground will create lots of jobs for professionals or soon to be experts. So, if you are considering prepping yourself for a lifelong career, here are the skills that you would need to master to land a machine-learning job.
- Basic knowledge & skill
You need a solid base of math, data science, and software so you might want to check yourself against these areas:
Statistics – they are used to create models out of the inputted data. Machine learning uses algorithms that are based on data analysis like variance and hypothesis.
Probability – machine learning is a powerful tool for making predictions. Therefore, you need to comfortably work with the principles of probability.
Data Modeling – machines need to be able to use data that is unstructured to make predictions. When there are gaps in the inputs, it needs to create a model of patterns and see what is missing.
CS and fundamentals of programming – machines use large data sets, as a result, you would need to be familiar with algorithms, computer architecture and data structures to handle this kind of computation.
Design – because machine learning uses a complex network of systems connected to one another, you will need a strong knowledge of APIs and design skills to keep up with changes that occur in the complex system you work with. Your system needs to be reliable and adaptable.
ML Libraries and algorithms – you will need to master the already existing libraries and models in development. In order to use available APIs and libraries such as Google TensorFlow or Apache Spark’s MLib, you have to understand how they work and when they can be applied.
- ML (Meta Language) programming
Machine learning does not use one programming language but has the flexibility of using the one that best suits each situation. This is why you will need ML skills for projects that require the use of such libraries in different programming languages.
C/C++ – this is the basis of programming and can help you create software that meets memory and speed criteria. Usually, machine learning engines rely on infrastructure build in C/C++ . Most embedded systems use this language and it is essential for future smart homes or cars.
Many ML libraries are also available for this particular language.
R – this language aims at statistical computing and data mining and does a great job with machine learning. Not that difficult and a lot of algorithms are based on it.
Python – accessible and universal, Python is a favorite when it comes to data processing in a fast, reliable way. It has many useful libraries for such purposes.
Look into the Future with Machine Learning
As time goes by, innovation brings machine learning more and more into our lives. Industries are ripping the rewards of using this technology already and experts are ever so ambitious. Take a leap and complete your skills to jump into the boat.