Machine Learning, abbreviated as ML, is the study of computer algorithms which allows the computers to act in a way when they have not in actual been programmed to do so. In the past decade, machine learning has given us self-driving vehicles, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.
Machine learning is so widespread today that we probably use it dozens of times a day without even knowing it. Many researchers also think that it is the best way to make progress towards human-level AI. Machine learning algorithms are also used in a wide variety of applications, such as fraud detection, spam filtering, malware threat detection, business process automation (BPA), predictive maintenance. Email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Advantages and Disadvantages of Machine Learning
Machine Learning has seen numerous uses ranging from predicting consumer behaviour to constituting the operating system of self-driving cars. However, it doesn't mean that just because some fields have seen its advantages, others will too.
When it comes to advantages, Machine Learning can help the enterprises understand consumer behaviour at a deeper level by collecting their data and correlating different behaviours at different points of time. Some internet companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the right advertisements in searches.
But it also comes with a few disadvantages, first, it can be expensive. These AI ventures are ordinarily determined by information researchers, who order significant compensations. These undertakings additionally require a programming foundation that can be significantly expensive as well.
There is likewise the issue of AI inclination. Calculations that prepared on informational indexes that bar certain populaces or contain blunders can prompt incorrect models of the world that, best case scenario, come up short and, to say the least, are prejudicial. At the point when an endeavour puts together centre business measures with respect to one-sided models, it can run into administrative and reputational hurt.
Prerequisites for becoming a Machine Learning Developer or Engineer
1) Advanced degree in Computer Science - Computer science fundamentals important for Machine Learning Engineers include data structures, algorithms, computability and complexity, and computer architecture.
One must be able to apply, implement, adapt or address them (as appropriate) when programming. Practice problems, coding competitions and hackathons are a great way to hone skills in this regard.
2) Good mathematical and statistical skills - One should have a great amount of mathematical knowledge, preferably in the field of probability and techniques derived from it. Closely related to this is the field of statistics, which provides for various measures such as mean, median and variance.
3) Programming experience in Python - The base language of Machine Learning is Python. It is widely considered as the preferred language for teaching and learning Machine Learning. Some reasons are:
· It’s simple to learn. As compared to other languages such as C, C++ and Java the syntax of Python is simpler and it also consists of a lot of code libraries for ease of use.
· Though it is slower than some other languages, the data handling capacity is great.
· Python is developed under an OSI-approved open source license, making it freely usable and distributable, even for commercial use. Python along with R is gaining momentum and popularity in the Analytics domain since both of these languages are open source.
· Capability of interacting with almost all the third party languages and platforms.
4) Choosing the appropriate field for yourself - After getting a gist of what Machine Learning is, one must choose the field they wish to go in. There are numerous fields associated with Machine Learning such as Deep Learning, AI, Data Analyst, Pattern Recognition, etc.
How to get hired as a Machine Learning Developer or Engineer?
Do you think you qualify for all the aforementioned prerequisites needed to become a developer or engineer in Machine Learning? Then what you would want to do next is build a portfolio and resume, the resume shall not simply be a piece of paper, it should be a personal website showcasing all of your projects and courses. After building an appropriate portfolio and resume, you should start applying for a job. You can do so by simply submitting an application on a company's online platform but you should not completely rely on that and focus on other things such as finding a recruiter on LinkedIn, ask a friend working in a tech company for a possible referral as more than half of the people working as such developers are hired through a referral. You should also attend job fairs and showcase your portfolio for a chance to grab a possible job in the company which clenches your interest.