Skills you need to become a machine learning engineer
Machine learning, or the analytical processes that let Artificial Intelligence systems detect and apply patterns, is a buzzworthy field. But what does it
Machine learning, or the analytical processes that let Artificial Intelligence systems detect and apply patterns, is a buzzworthy field. But what does it take to create a machine learning engineer? They straddle the fence between software engineering and data science. In order to build machine learning algorithms, they need to draw their knowledge and experience from both fields. Interested in taking up the challenge? Read on to find out some essential skills you need to become a machine learning engineer.
Software engineering vs data science
A machine learning engineer must develop effective software that automates predictive models and make use of big data. Therefore, they need to be familiar with software engineering and data science. Both are highly technical fields, and they have similar skill sets to a certain degree. But there are some significant differences.
Software Engineering
- Focus mainly on software development lifecycles and object-oriented programming
- Must know more than Python, depending on whether they are frontend/backend engineers
Data Science
- Focus mainly on machine learning algorithms
- In general, Python is the only language they need to understand
- Work tends to revolve around data and data manipulation for models
- Has a focus on statistics and data analytics
Essential skills to become a machine learning engineer
Software engineering and data science are necessary for a machine learning engineer. The focus of a machine learning engineer is to create software components that are capable of functioning with minimal human interference. That is why the above technical fields are helpful for his or her role. However, there are additional skills you need as well.
Probability and Statistics
Often, probability – both in the form of formal characteristics and techniques – are at the heart of machine learning algorithms. Associated with probability is the field of statistics. Probabilities offer machine learning engineers to counter unexpected events in the real world. However, statistics give them measures, distributions, and analysis methods to build and validate models based on big data. Therefore, both are necessary skills in order to become a machine learning engineer.
Computer Science Fundamentals and Programming
Machine learning engineers also need to be familiar with Computer Science Fundamentals. These fundamentals include
- Data structures
- Algorithms
- Space and time complexity
If you have a Bachelor’s in Computer Science, these are concepts are will already know. Therefore, having a Bachelor’s in CS is a great boon in that you don’t have to learn these concepts from scratch!
Versatility in programming languages is also a requirement for your chosen field. Python is one of the most popular programming languages among machine learning engineers and data scientists. In addition, you may wish to be fluent in Spark and Hadoop, SQL, Apache Kafka, etc.
Applying Machine learning libraries and algorithms
There are standard machine learning algorithms available through libraries/packages, APIs, etc. However, the knowledge to apply them is something you need to acquire. This is because, in order to effectively apply them, you need to choose a suitable model, learn the procedure that suits the data and understand the effect of hyperparameters on learning.
Without understanding the different algorithms, you cannot integrate them with different systems and may face many difficulties. There are Data Science and machine learning challenges you can try out. These challenges offer exposure to different problems that may arise and how to solve them effectively.
neural networks
Neural Networks mimic the neurons in the human brain. Just as neurons are important for humans, so too are Neural Networks in machine learning!
There are multiple layers within the Neural Network
- Input Layer – receives data from the outside world, which gets passed on to the…
- Hidden layers – these multiple layers transform input into data, which is vital for the…
- Output layer
There are different types of Neural Networks. And while you don’t have to understand each type in detail, you do need to grasp their core facts.
applied mathematics
If you like maths, this is fantastic for you. But if you don’t, you might be in trouble. Now, you might be asking why math is a necessary skill to become a machine learning engineer.
Well, there are many uses for maths in machine learning. Mathematical formulas, for an instant, can help you to select the best algorithm for your system. You can also use it to set parameters for your model. Strong knowledge of mathematics will also help you to understand many machine learning algorithms better. This is because these algorithms are derived from statistical modelling procedures.
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