What is machine learning?
Well, that has been a term we have heard in the recent period quite a number of times. As the name suggests, does it simply means that a machine learns by itself? It is high time, we as common people get to know about machine learning and understand the technicalities behind this concept. So, jump into the article and let us see what is behind this and get to know the machine learning basics.
We, as humans, could work and enforce ourselves in search of a data or analysis only when we are guided through and supported. That’s our nature. But, as a surprise, we have the answer to a question asked above. Machine learning is all about the machines we use like computers enabling themselves for self-learning, without having to program it manually at any point of time. It is a subcategory in the field of Artificial intelligence (AI) and this entire idea is based on the concept of creation of systems that can self-learn from data. This term “machine learning” was coined by Arthur Samuel, an expert and pioneer in the field of gaming and artificial intelligence, in the year 1959 at IBM. Adopting to new areas and data, the computer systems learn to change, develop on its own. This has been widely prevalent and used worldwide in various fields and tech fronts.
Does it really matters?
In today’s world, machine learning has taken an integral part of life with our day to day activities. The Facebook suggestions, self-drive Google car, online recommendation engines basically in Netflix showing movies you might like or “items you can consider” in Amazon, etc; are all determined and analyzed through this machine learning. It involves the process of filtering of the wide range of availability of data, narrowing down to what we need without having us to manually program the computer to search for. Simply speaking, it scrutinizes the available data based on our needs. As we could see around, this tech aspect is being widely prevalent and used in many fields. Through this, we could analyze that this is an open field and could be even more developed or enhanced with the proper research background. There is a lot of scope for new developments here.
The process behind machine learning:
The major process behind Machine learning concepts would be broke down into three parts that basically constitute the machine learning working system.
So, what does these three parts do in this machine learning process?
Basically, it all starts with the model. This model machine learning system consists of the basic input of data which has to be manually done by the human being. This serves as a base for prediction. With the input of the data, the further calculations related to this model are made by the parameters based on assumptions. This is where the outputs would be predicted at a level based on the input given. On further exploration, you could find out a system pattern that could be analyzed with the output, thus derived. A mathematical equation is determined and thus used for calculating the output data. After the model is set, the actual data is entered into the machine learning algorithms. These are termed as ‘training data’ or ‘training set’, being the raw data in hand, without which we cannot arrive at the actual outputs, we tend to look for.
Here, comes the main work of learner. This is where the actual process of machine learning lies. A stable comparison is made between the predicted data and actual data and more math is calculated to adjust the initial predictions made. So, after the adjustments are made, the system is run again with the new set of results. Another comparison is carried out with the entered data and the adjusted data. However, these calculations would most probably bring you much closer to the actual determined output. This process has to be continued until the actual result is arrived at with perfect accuracy and expected outcome, much closer to the training data we already have in hand.
The process of making those small adjustments is simply termed to be ‘gradient learning’. The gradual increase or changes in the calculated data, making very small changes, is what this entire machine learning process works on. This is like going down the hill. Not so fast, making the travel downhill slow and steady. The math with which this is calculated totally depends on the calculus and basic mathematics probability solutions.
Long back, the entire process seemed too long and time-consuming, considering the small-sized computers. At present, this has widely evolved, making the process much simpler and easier.
As mentioned above, this process has a common usage in our day to day life. This is mainly used to identify images, videos, events, etc. in the computer systems. The real-time ads you watch while surfing through websites or in social media, take this usage. Ever wondered about the fact that, how do you get the recommendations perfect and most needed? It all comes with this practice of machine learning. At the end, it is all about analyzing the data using trial and error method.
Classifications of machine learning:
Classified broadly, there are three types of machine learning.
- Supervised machine learning.
- Unsupervised machine learning.
- Reinforcement machine learning.
Out of these three, we could most probably see the first two of them in our regular common usage.
Python machine learning:
Python machine learning has been one of the key advancements in the process of understanding and learning about machine learning techniques. Python is a widely used interpreted language for coding. To understand the working behind machine learning in Python, you could download the Python SciPy and install it. After this, you could try out minute modules and work out small projects. By doing this, you could understand the actual mechanism and technicalities behind the machine learning process. Come what may, nothing gives you much better learning than you practically experiencing it with your own projects. Through this, you would have to install the software and thus, try out the basics.
There are 5 libraries in this python, which basically constitutes the entire software system.
How to master machine learning:
Admitted that, this is a complicated system and it is too complex to master the entire machine learning process. Considering the huge availability of data resources, many people get lost amidst the learning process. So, here is a short summary of step by step learning of machine learning.
1st step: Get to know the basic of ‘Python’ or ‘R’. This is because these are the most commonly used interpreted languages for coding.
2nd step: Understand the descriptive part and probable statistical analysis. Udacity offers the best machine learning courses with such deep understanding of the process.
3rd step: Explore the data by data cleaning and feature engineering. Follow the algorithms and techniques for better perceptions.
4th step: Get yourselves to participate in basic Kaggle competitions. This has its level down and at the least possible standards. Enroll yourselves in this and get to know how this works. Get to know about deep learning and ensemble modeling.
5th step: Enroll in higher standards of kaggle competition.
We get to know that, this mostly depends on practical exposure than the theoretical knowledge and theory workouts. You have to practically work it out with system sample models and learn through such analysis.
References and Guides for machine learning:
The internet is full of online courses and pdf’s that contain huge quantum of data comprising the entire machine learning activities and processes. You could refer those for the better understanding of the system modules.
Some of the machine learning courses that you could take up online as paid courses or for free are-
Once you enroll in these courses, they give you the best teachings that make you a professional expert in the field of machine learning. Also, there are some machine learning guides and machine learning books, which you could read and get a better idea of how this works.
- Machine learning in action by Peter Harrington.
- Python machine learning by Imusti.
- Hands-on data Science and Python machine learning by Frank Kane.
So, these are some of the books you could refer. They serve you as the best guide for your learning.
There are also certain videos on Youtube. You could learn how this works from those videos also.
But whatever be the guides or references you take, it just shows you the right track to work upon. It is upon you to travel the path and become a master of it. We believe that these machine learning tutorials would have been effective in your learning process. Being widely used today, it is assured that the future lies on this with a major impact on the society. So, learn the better and experiment with it. Be a professional!!!!