Machine Learning - What It Is and Why It Matters?

29 Nov · 6 min read

Machine Learning - What It Is and Why It Matters?

As commonly known, Machine Learning is a subset of Artificial Intelligence, used as computational science which focuses on analyzing and interpreting the pattern and structure of the data to enable self learning, decision making ability, reasoning without much interference of human interaction. In simple terms, Machine Learning, allows users to feed the computing algorithms to machines to analyze and make data driven decisions and recommendations by self learning. If the results come out correctly, well and good, if not, algorithms can be manipulated to work accurately for the future decision making capability. 

Machine Learning Types

Machine Learning is a complex domain, hence it is divided into three parts. Each one has its own significance, learning and different results by utilizing the various forms of data. Where, around 70% of the Machine Learning involves Supervised Learning, the 10-20% contains  Unsupervised Learning. And the rest includes reinforcement learning. 

  • Supervised Learning: Supervised learning, as the name suggests, is done under a supervision. Where the data is trained under a supervision, directed into a successful execution. Once the model is trained, one can use unknown data into the structure algorithm to cross verify the learning output is correct or not.

Some of the top algorithms used for Supervised learning are Polynomial Regression, Linear Regression, Logistic Regression, Decision trees, K-nearest neighbors.

  • Unsupervised Learning: Unsupervised learning is being done without any supervision, which means data is not labeled nor looked before. Without the awareness of data, input can’t be guided into the algorithm, hence named as Unsupervised Learning. Where data is fed to an algorithm and is used to train the data model itself. The trained model tries to recognize a pattern and gives the response accordingly. The top algorithms used for Unsupervised Learning are Fuzzy means, Partial least squares, K-means Clustering, Apriori, Singular Value Decomposition.
  • Reinforcement Learning: Reinforcement Learning works as a traditional data analysis process, where an algorithm discovers the data set through a hit and trial process and determines the results on the basis of higher rewards. The three major components of Reinforcement Learning are The Agent, The environment and The actions. The agent acts as a learner and decision maker, the environment is everything , the agent reacts with and the actions are the resultant of what the agent does.

Machine Learning Workflow

Machine Learning workflow is divided into three main steps:

1) Gathering of Data: Gathering data is dependable on the type of the project we wish to work on, for instance, if it’s a ML project containing real time data to process, then it can be done through different IOT sensors. The dataset is collected from various sources such as file, database or any other format. And once a dataset is collected it is processed for Data Preparation as it can not be pushed for analysis due to discrete data. Hence the data preparation process is done.
Some free datasets available on the internet like UCI machine learning repositories can also be used for datasets. Kaggle is also one of the most visited websites used for practice of Machine Learning Algorithms. They also host interesting competitions where people participate to test their knowledge of Machine Learning.  

2)  Data Preprocessing and Research of the Learning Model:  After the data is prepared, data preprocessing is done where the data gets cleaned in a structures process, which includes conversion of data, filling the missing values, outlier detection and then the next step is performed. Now, the data is analyzed that which model of machine learning will be the best suited, supervised or unsupervised, and it is classified then in categories defined, such as “red”, “blue”, “spam” etc..

3) Training and Testing the model on Dataset: For training and testing the model, data is splitted into three parts, ‘Training data’, ‘Validation data’, ’Testing data’. Training data is implemented to build the model, test data is set to validate the model. Once the data is tested, data is validated into different parameters, such as, ‘True Positive’, ‘True Negative’, ‘False Positive’, ’False Negative’. And after that the data is sent for the Evaluation phase. 

Significance of Machine Learning

Machine Learning has become a part of our lives in small things, for example, when you call Alexa! And ask a question, you get a response, but ever wondered how this process is going on in the backend? Here is the fact busted! The Alexa is a cloud- based voice service device, which has N number of AI algorithms saved and computed through NLP. So it goes in the backend within microseconds and gives your prompt response. This is just a small part, ML has become a very important part of our life in many ways. PayPal, Facebook, IBM, are some more famous names working and growing in the ML industry. Let’s understand how ML is significant in our lives.

1) Machine Learning improvise Admin work

Machine Learning rescues the admin work and helps in automation, which is the first and foremost goal of AI and ML. Daily tasks such as scheduling, chatting, etc. are much more minimized by smart solutions. If we stick to our previous example Amazon Alexa, it listens to your commands and follows the order to reduce your manual tasks.

Image recognition has also been very helpful in our daily lives reducing the manual stress, such as facial recognition, in general identity verification. Government agencies are also making good use of Machine Learning use cases. Moreover, it is very helpful in eCommerce as well.

2) Machine Learning empowers Technology

You name a thing, and there is a ML solution available for it. In this tech growing era, we have no time to sit and do the tasks manually, as with growing technology, we want smart solutions to rapidly pace up. And ML is doing exceptionally well there too. The industry has got so many ML solutions, and accompanying the technology together. Here are some examples:

  • Defence
  • Healthcare
  • Manufacturing
  • eCommerce
  • Agriculture
  • Advertisement, Marketing and Sales and of course
  • Software Development

3) Machine Learning lowers the Complexity of Work

In a world where data is everything, it is complex too. And the technology in the past 10-15 years has been working on leaning to less complex data. Tasks requiring tons of admin work have been eliminated by 80-90% by the help of machine learning, which is a commendable growth. Gone are the days, when people used to sit at their desks and do the admin job. 

Future of Machine Learning

It’s just the baby steps, which ML has taken, there is a lot more to go. By the next decade, a radical change is expected, in the way it will affect our daily lives. People are getting more comfortable and adapting to the change slowly with growing technology. In a study, Garter says that it wouldn’t be surprising:

  • Where ML takes control in every aspect of human life
  • Be omnipresent is all the industries
  • Change in the shape of data
  • Enter in cloud based services

Final Notes

Machine learning has a bright future, yet a destructive aspect too. It’s purely the intention of tech giants, that how do they make the use of it. The world is already witnessing the glimpse of it in a productive way, in healthcare, food, manufacturing, and many other fields. It’s up to people and techies to give a direction, to the very famous debate, if it’s a boon or bane.

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