How to equip yourself with the tools of machine learning?

This post aims to understand the core concepts of machine learning and how to become familiar with these core concepts. It has to be noted at this point of time that the proper understanding of machine learning is incomplete without the knowledge of various tools that the language uses. As such, the post is motivated by the techniques to equip yourself with the tools of machine learning. An effort has also been made to examine if python machine learning is too herculean a task to overcome.

Understanding the concept of machine learning. 

Machine learning is a process or a technique or a pedagogical procedure by which the machines are able to improvise upon the various tasks which they have been carrying out. To take intelligent decisions regarding the minimization of repetitive tasks and maximization of efficiency is at the very core of machine learning. As an analogy, we can imagine the robotic arm which is devoid of neurons but learns over a period of time to acquire grips over different objects differently in an intelligent setting. This can be envisioned from the fact that the grip to hold a glass of water may be different from the grip to hold a flower vase as both objects have a lot of difference in their shape. If you are wondering how does a robotic arm learn to grip different objects differently, then the answer is probably the artificial neural schema provided by nothing more than machine learning.

Variety of languages and tools used in machine learning 

The most important language used in machine learning is python. Python, a general-purpose language which is known for its distinguishing  features like readability, good structure, and a relatively mild learning curve continues to grow in popularity and fame. According to the Annual Developer Survey conducted by Stack Overflow, Python can be called the fastest-growing major programming language. It’s ranked the seventh most popular language and now is one step ahead of other popular language called C programming.

Data analytics and visualization tools are increasingly helpful in understanding machine learning language. Let us take a quick tour of few.

One of the famous Python data analysis library which enhances analytics and modelling is “panda“. This library was designed by Wes McKinney with the sole purpose of making data analytics simple and convenient.

Another important library is matplotlib which serves the most important purpose of visualizing the data in a two dimensional form.

Last but not the least, Jupyter Notebook is an excellent application which enables us to share documents and files with a live code. It also enables discussion on end results. Thus the Notebook has diverse functionality and us helpful in lot many cases.

Equipping yourself with the tools of machine learning 

Equipping yourself with tools such as python machine learning is not a herculean task at all. This can be envisaged from two basic facts. The first one is that for understanding any learning language, it is important to understand its script. Hence, to move on to machine learning without understanding libraries is definitely a wrong approach. Secondly, previous conceived notions add to the difficulty in understanding the language. One of the best advisories for understanding python machine learning is to seek the expert guidance of training institutions online. They ensure that you get familiar with python in a short span of time.

Throwing caution to the winds 

It is often argued that machine learning is futile as there is always the fear of machine becoming it’s own master. The fear can be deemed as illusionary and is best explained by ‘GyanNagpal’ as a symbiotic relationship with a word of caution.

‘GyanNagpal’ in “The Future Ready Organization: How Dynamic Capability Management Is Reshaping the Modern Workplace”, quotes:

The relationship  between man and machine is better imagined as a partnership with machines employed to do work too repetitive or rudimentary for human intelligence, too dangerous for human well-being or too complex for human time. Human capability, on the other hand, has always excelled at fashioning such machines, creating meaning from the unfamiliar and in imaginative pursuits. In such an arrangement, technology does replace human effort, but only in areas where human effort is sub-optimal.”

Concluding remarks 

We can conclude that it is possible to equip yourself with the tools of machine learning by following a step by step process, the first of which includes a cursory understanding of the basic concepts. When that is done, the road becomes all set to imbibe the language of python. With python and other tools understood, you have already mastered the art of machine learning!


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