Machine learning and software engineering, these are two paths you can select to forward your career in. Coding is involved in both careers so there is no escape from it, unless you choose a career where it does not apply like UI/UX design. When it comes to machine learning engineering, it is about writing programs and training it for desired outcomes. Either the machine is software or hardware combined with software on the backend.
One cannot compare ML and software engineering because they are two different types of work. However, ML involves a lot of research and experimentation, it also requires a different mindset and approach to solutions than software engineering.
Machine Learning, what is that?
Machine learning, a.k.a. ML, is a sub part of AI, artificial intelligence, it involves algorithms and data that helps machine learn. From mobile assistants to self-driving cars and whatnot, machine learning is used everywhere and every industry. The goal is to make human lives easier.
Machine Learning vs Software Engineering?
Software engineering is generally referred to as coding. Here ML engineers write softwares based on logic and algorithms that help complete tasks. On the other hand, machine learning engineering allows computers to create rules for task automation and allow them to learn how to perform better.
For instance a web app or mobile app is an example of software engineering while voice assistant and self driving software are examples of ML engineering.
Factors making Machine Learning harder
The fact that when you hire machine learning engineers they are required to have knowledge of various aspects and fields makes ML hard. Statistics, mathematics, programming, data collection and cleaning are mainly the aspects that ML engineers should have deep knowledge about. In addition other programming aspects like debugging, algorithmic building and selection, and algorithm optimization are very important and equally important to have in depth knowledge about.
Programming knowledge
Programming languages are used to write machine learning code. So, learning one is essential. Python, R, C++, and JavaScript are the ones used for machine learning.
Deep learning
Deep learning, a subset of ML, is the field where programmers make software to replicate human thinking. Deep learning utilizes three or more neural network layers in order to gather insights deeper than a single layer could have managed.
Distributed computing
Utilizing a computer network for scaling means, is distributed computing. It combines cloud computing with engineering. Machine learning applications are trained and designed to use
computers networks for scaling up. This gives a power up thus the program runs easily with saved energy costs.
Algorithmic computing
When you hire machine learning developers, most of the time they will be using pre-made algorithms with little to no tweaks. However, sometimes they would have to optimize the algorithm for a specific need. This needs in-depth knowledge and experience, even after trial and error is still required.
Math and stats
Machine learning is a combination of a lot of things, of which statistical and mathematical concepts are very important. From linear algebra to probability and statistics everything is included and will be used sooner or later.
Does machine learning require coding?
The short answer is yes. The long answer is also yes, and here is the explanation. The coding part can be anywhere from a little to too much. This depends on the role and type of industry you are working in along with other factors. To implement ML and the concepts you can think of easily as humans, a strong grasp of programming and algorithms is required. Obviously, to implement you also need to have a strong grip on a programming language.
C++, Java, and Python come on top of which Python is most popular. However, as a ML engineer you have other options like R, Lisp, and Prolog. Understanding of HTML and CSS isn’t necessarily but will give you additional benefits making you stand out.
Is Machine Learning Difficult?
When compared to a full stack developer, machine learning is not as hard as it seems. Full stack developers have to master various aspects like database, frontend, backend, APIs along with other things like getting knees deep into a framework for both front and back end. Inversely, ML engineers have to get command over other aspects, mentioned above. It is not hard but different, thus requires a different mindset.
Comments