Machine Learning is one of the most discussed topics in recent times. May it be the medical field, businesses or education every domain is partially or fully dependent on machine learning in some way. Every second person is either discussing it or getting affected by it. So it becomes very important to know about it and if possible then learn it.
Some of the most technical topics such as Computer science, Electronics, Physics, and Mathematics are taught using a bottom-up approach. This methodology of training complex themes includes spreading out the points in a legitimate manner with a characteristic movement inability. The only problem with us the Humans when learning new technology or adapt to new things and need motivation in the form of tangible results.
But when we compare the several skills learned by us which include cooking, driving, playing music, etc., these were learned using the top-down approach of learning. A similar approach can be applied to learning Machine Learning. This would not only make it easier to learn but fun as well.
In this article, we will see how we can approach learning Machine Learning in a fun way and also we will explore how to approach machine learning, the skill set needed and how to approach learning.
But before we delve into learning machine learning and knowing how to begin learning Machine Learning, let’s share a few numbers. According to the Monster.com leading website for Job searches, Software Developers is the best jobs in 2019, Money.com also asserts Software developer as the best jobs in 2019 according to their survey. And finding out and surveying further we find in the software developer category the best jobs are in Machine learning and Python. Also, according to Indeed another job search portal, Machine Learning is the fastest-growing job in the world.
These numbers are enough to justify the need to learn Machine learning and do it as soon as possible.
Where to begin?
Machine learning is a vast and diverse field. So to jump in you will need to have some basics needed to start machine learning. Before we begin let me clarify, these methods are based on my experience of learning and teaching machine learning for a long time. This is not an exact guide or only roadmap. But, for sure, easier version to follow to learn Machine learning indeed.
To delve deep into machine learning, you need a set of skills and concepts. These concepts include Mathematics basics, Linear Algebra, and statistics. Also, primary knowledge of python programming language will help you to begin well. But let me add you don’t need to have a Ph.D. in mathematics. Basic knowledge will be good to go. Just to summarize these steps:
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Basics of Python
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Linear Algebra
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Statistics
Google and Explore LinkedIn
A lot of people these days are learning Machine learning and its application with the sense of getting jobs, switching their careers or maybe developing products which can fetch more market base. In all of these, I would advise you is to Google recent trends, flip through news and blogs to find out current applications, and associate the needs of companies via LinkedIn and job advertisements. This will give you a very good idea of what exactly is expected of a Machine learning expert hence giving you a good idea of where you would want to reach down the roadmap.
Learning ML terminologies and Concepts
The above step is done properly will leave with you lots and lots of questions and half-understood information due to lack of Machine learning terminologies in your regular vocab. Hence start understanding those terms and concepts via different sources. Terms such as Model, Label, Training, testing, prediction, accuracy, efficiency will occur quite frequently. We will discuss these in upcoming articles as to what they mean and how they are important. But for now, start exploring these terms and their meanings from various sources. Try to figure it out in your way.
Also, try to understand various types of models, algorithms that are part of machine learning. These include supervised learning models, unsupervised learning models, semi-supervised and reinforcement learning models. Read various applications and see which is more suitable for your learning and start with implementing existing codes. Because more and more practice of such models and their implementations will get your errors solving skills better too.
Get the right Mentor to Test your Knowledge
This is a very important step to your learning. Whatever you have gained from all the steps needs to be validated and checked. And to do this and further plan the learning curve, it is very important to have a good academic or industrial mentor. Experience of working with the right professional and following qualitative practices can make your career in machine learning. It’s not always easy to find the right mentor who can shape your careers, many options available online suggest various techniques for the same. But what I have learned from my experience is that income-sharing can be the right way to spark collaborations between professionals and novice learners.
If not ready for the same online portals are providing professional mentorship on various topics, you may sign up for the same and reap the benefits of a large network pool of experts.
After following all these steps religiously you are well on your way to become Machine Learning experts and you can keep improving your aptitudes by taking a shot at an ever-increasing number of difficulties and in the long run making increasingly inventive Machines Learning projects.
