In this growing world, technical achievements are playing a vital role. We must have heard that technology has started to solve some of the old unsolved problems quite easily. Machine Learning is one of such technologies which is solving many problems across wider verticals. ML is helping doctors in the medical industry, armies in the defense, farmers in the agriculture domain, scientists in their innovation, and many more.
Machine Learning and Artificial Intelligence are some of the hottest topics that can ensure a brighter future for any individual in the upcoming decade. As per Indeed’s post, Machine Learning Engineer is the top-rated job among the best jobs of 2019. The average salary of an ML engineer is $146,085.
But the technologies like AI and ML are new in industries; hence becoming a machine learning engineer or artificial intelligence engineer is not so trivial. Taking the first steps in this field is challenging not because there are fewer resources available over the internet, but most of the resources are unstructured, and initial learners give up here. So it's always necessary for us to first find the proper guidance which is structured in such a way that it can keep us motivated throughout our journey.
In this article, we will be sharing definite 6 steps of learning which actually were followed by several industry professionals working in the ML industry. These steps may differ from conventional methods, but we can assure you that these steps work.
Python is the most commonly used language in the field of machine learning and deep learning. Hence we would recommend getting familiar with the basics of this programming language. To learn Python, we can pursue either of the two options :
Note: There are other programming languages like R, Scala, Julia, but we are emphasizing Python because of six major reasons shown in the image below.
To start the learning of any new technology, we must know answers to some basic questions.
We can find the answers to these questions inside two of the blogs here in which the first four questions have been answered in detail, and here the fifth question is answered with an example. The more we dig, the more enriched our learning will be. This step is really essential if we are interested in making our career in this domain.
Note : We are absolutely free to keep this step in parallel to later steps, as starting from maths may be tough/boring if we have not strong mathematical background and we personally believe in the philosophy of “Learning by Doing” .
To become an expert in Machine Learning, we should know the basics of some most frequently used maths inside Machine Learning. Concepts like Linear Algebra, Probability, Statistics, and Calculus can be considered the machine learning basic building blocks. We need not master these concepts as only some parts of these concepts will be useful in problem-solving. We should learn these concepts not only from the theoretical angle but also from the python libraries' angle. This means we must get familiar with the python libraries that actually help us in such mathematical calculations.
Below are the links for the free courses which will introduce you with,
Python libraries that are very useful and must to explore are :
With these libraries' use, one or two code lines can implement hundreds of mathematical implementation lines. Not only this, these libraries optimize the calculations a lot so that our algorithms perform more than 10X times faster.
We have divided this step into two parts where both the parts work in parallel,
This step is one of the most crucial steps in the field of Machine Learning.
There is one famous saying that,
Ability of extracting the useful information from the given dataset is the key to differentiate between an average and better Machine Learning engineer.
The machine learning model works best with pre-processed and meaningful data. In this step, we should also explore some python libraries specially designed to help us in the pre-processing steps.
Python Libraries that are helpful for this step would be :
We can go through some online courses to know more about data-preprocessing things. Courses that we can have a look at to master the data pre-processing steps are :
But keep in mind that the depth of this field is also infinite. We will look at selective things which are required to be an expert in the field of machine learning.
As we said, there is no point in learning things if we did not practice that learning. So we would recommend finding some open-source datasets that we can find here. We can download any dataset and try to implement the learnings of part 1.
Task: For example, download the COVID-19 dataset, clean it, find the region with a maximum number of Covid cases.
This step also includes two parts, where part 1 is again focused on learning, and part 2 is focused on testing the part 1 learning.
At this step, we are fully ready to start our journey towards machine learning. We can start going through any basic course.
There are many other resources that we can find on the internet. Still, as we focus on the language python because of the tremendous support available on the internet, we would recommend learning ML with scikit-learn. We should always try to be in the depth of the concept and try to find the answer of What, Why, and How of any concept or algorithm.
Python library that would be great to explore
This step is a must if we want to be an expert in the area of Machine Learning. We have seen many professionals who have good certificates, but they cannot solve the problem.
Hence we would strongly recommend solving at least one problem statement available in the free domain. This would give us enough confidence to solve the related problem in that area. We don’t need first to complete the full course and then try the problem. It’s the older way which is slow. We can pick any one problem of simple complexity and solve it step by step with the internet's help keeping the course going on in parallel.
After following the above steps, we can become good ML engineers with hands-on experience. We can start with some moderate complexity problems and then the harder ones. If we really want to become passionate about it, we can collect some research papers in the field of Machine Learning and try to implement them on our own. It will make our experience better and make us confident enough. We can try solving one problem based on each algorithm covered in the course.
The overall process for the machine learning model development can be seen in the above image. Model deployment can be removed from the picture, but it's always good to build something completely and offer it as a complete solution.
Repeat the same strategy of Step 4 and follow the philosophy of Learning by Doing.
Deep-Learning courses that are really great
We also have a completely different way of learning the Deep-learning concept, but that also includes the above courses. We would share that pathway in a separate blog.
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