Would you like to change your career and become a machine learning engineer?
Artificial intelligence, machine learning, data science, and other technologies are growing in popularity right now. However, these technologies are also used casually, and many people are unaware of their true meanings or the abilities needed to learn them. This article can help you with that by outlining all the main qualifications you must possess in order to land your ideal position as a machine learning engineer!
Machine learning is a field of technology that focuses on how computers may learn on their own from data. This is done without much human input or explicit programming. This is a complicated area that is a condensed form of AI and is connected to other technologies like data science. This raises the question, though, of what a machine learning engineer actually is. What sets him apart from a data scientist or analyst? Let’s first clarify this.
What Is A Machine Learning Engineer
Because Machine Learning and Data Science are closely related. There are some parallels between the job descriptions of a Data Scientist, Data Analyst, and Machine Learning Engineer. But the major differences lie in what they stress. Drawing conclusions from the data and communicating them to senior executives so they may use them to make important decisions is the main objective of data scientists and analysts. Knowing something about ML algorithms is required for this though.
Let’s look at the qualifications you need to work as a machine learning engineer. While the first two of them are pretty fundamental and you might have even learned them in high school or your undergraduate degree, as you proceed down the list, they become more sophisticated and domain-specific.
Qualifications for a Career as a Machine Learning Engineer
1. Fundamentals of Computer Science and Programming
This is another prerequisite for developing into a competent machine learning engineer. Different computer science concepts, such as data structures (stack, queue, tree, and graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, etc., must be familiar to you. The good news is that if you have a bachelor’s degree in computer science, you presumably already know all of this. You should be knowledgeable in a variety of programming languages, including Python and R for machine learning and statistics. Moreover, you should be familiar with technologies like Spark and Hadoop for distributed computing, SQL for managing databases, etc. It’s awesome if you are familiar with Python’s libraries, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc. Python is a widely popular programming language, especially for Machine Learning and Data Science.
2. Statistics and Mathematics
A machine learning engineer’s arsenal of skills includes math, which is highly crucial. It is also among the fundamental subjects taught from the start of school, which is why has a high rank on our list. But do you ever question why math is even necessary? In particular, if you don’t like it? In ML, math can be used in a variety of ways. The right ML algorithm for your data can be chosen using a variety of mathematical formulas. You can also use math to set parameters and approximation confidence levels. Since many ML algorithms are applications derived from statistical modeling procedures, it’s very simple to understand them if you have a strong background in math.
You should be familiar with important math concepts including linear algebra, probability, statistics, multivariate calculus, and distributions like Poisson, normal, and binomial, among others. A basic understanding of physics ideas is also helpful if you wish to work as a machine learning engineer.
3. Machine Learning Algorithms
What is a crucial competency for a machine learning engineer? Knowing all the popular machine learning algorithms is obviously crucial so that you can decide where to use which algorithms. The three most prevalent categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning algorithms. Apriori Algorithm, Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Linear Regression, Logistic Regression, Decision Trees, etc. are a few of them. So before starting your career in ML, it’s a good idea to have a solid understanding of all these methods.
4. Data Modeling and Evaluation
Data modeling and evaluation skills are essential for machine learning engineers. Finding patterns that are hidden from the naked eye requires first understanding the underlying structure of the data. The data must also be evaluated using an algorithm that is appropriate for the data. For instance, the data determines the appropriate machine learning technique to apply, such as regression, classification, clustering, dimension reduction, etc. A regression approach for accuracy might be a random forest, whereas a classification algorithm for vast data and speed might be Naive Bayes. Similar to how k mode for categorical variables and k means for probability are used in clustering. To properly contribute to data modeling and evaluation, you need to be aware of all the specifics of different techniques.
5. Neural Networks
The significance of neural networks in the work of ML engineers cannot be overstated! The neurons in the human brain served as the inspiration for these neural networks. They have a number of layers, including an input layer that takes information from the outside world and transforms it into information that is useful for the output layer through the passage of several hidden classes. These exhibit a profound understanding of parallel and sequential calculations that are applied to data analysis or learning. There are numerous varieties of neural networks, including Feedforward Neural Networks, Feedforward Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, and Modular Neural Networks.
Naturally significant and an essential component of machine learning, natural language processing is vital. In essence, NLP tries to instruct computers in the complexity of human language. This is done in order for computers to comprehend and interpret human language, eventually leading to a better understanding of human communication. The foundation for Natural Language Processing is made up of a variety of libraries. By breaking the text down according to its grammar, extracting key phrases, deleting unnecessary words, etc. These libraries can be used to help computers understand natural language. You may already be acquainted with a few or perhaps one of these libraries, such as the Natural Language Toolkit, which is the most well-liked platform for developing NLP-related apps.
7. The most important skill
Finally, we reach a soft skill that might not be regarded as being all that crucial. However, having strong communication skills is the most important one and it can drastically alter your job trajectory. This is due to the fact that, even though you are the best at understanding the data and the insights produced by machine learning, it is equally crucial that you are able to communicate these findings to a non-technical team, your shareholders, or clients.
This may also entail employing data storytelling, in which case you must be able to present your data in a narrative fashion with a beginning and an end at specific outcomes you have discovered by applying machine learning to the data. This is so that a firm may focus more on the actionable insights that can be drawn from the data, which are ultimately more essential to a company than the data analysis. And if you can successfully communicate these thoughts, the sky is the limit for you in your line of work!