Getting Started With Machine Learning | Beginners Guide to ML

 Welcome To the world of Machine Learning.

We are going to deep dive into Machine Learning and feel the real meaning with all its applications and advancements by discussing every aspect of the real-world scenario where Machine learning takes its stand.

Note that, Machine Learning is a branch of Artificial Intelligence, and as a beginner, visualization in this field is essential to excel in the field at a higher level so, be calm and enjoy each step with joy as you move forward in your data science journey.

Table of Contents

  1. Introduction to Machine Learning
  2. What is Need for Machine Learning?
  3. Classification of Machine Learning
    • Regression
    • Classification
    • Reinforcement Learning
  4. End Notes

What is Machine Learning

In the real world, we are surrounded by humans who can learn everything by experience with their learning capability. but think about the point, can machines learn from the experience like humans do?
The answer is YES. Machines can also learn from past data.

Describe Machine Learning
The term Machine Learning was introduced by Arthur Samuel way back in 1959.  Machine learning is a subset of Artificial Intelligence that learns from past data and helps to draw business decisions on the upcoming new data in the future based on multiple parameters.

" Machine Learning enables a machine to automatically learn from data, improve performance from experience, and predict things without being explicitly programmed "

With the help of the sample of data which is known as training data Machine learning algorithms build a Mathematical model that helps to draw future decisions without being explicitly programmed.  

Need for Machine Learning

Now, you all are wondering with certain kinds of questions. Why the term Machine Learning has evolved. without this the decisions cannot be drawn and what kind of change does it provide to a business?.

As today we are living in a big data era, the data is generated at a massive rate in huge amounts and to tackle the enormous data or to store, process, and make a certain conclusion on that data for the human is an impossible task, so is there any way that once we make a certain model which is capable to take all this burden from human and handle the situation excellently and provide with a better conclusion. Hence, here came the evolution of Machine learning to make things easy for us.

Now, I am pretty sure that you are capable to understand and visualize the situation in a better way.

The importance of Machine learning can easily be understood by its use cases, currently, Machine learning is used in self-driving cars, fraud detection by cybersecurity, friend suggestion, and tagging by Facebook.

The factors which show the importance of Machine Learning

  • A rapid increase in data day by day.
  • Solving complex problems, which is difficult for humans.
  • Finding hidden patterns, extracting meaningful information from large documents.

Classification of Machine Learning

Machine Learning can be classified into 3 types

1) Supervised Learning

Supervised Learning is a type of learning in which labeled data is provided to a Machine learning model to train it and to predict the outcome of the new data. In simple words, as the name suggests, In this technique, the model works under the supervision in which the model is provided with a sample of labeled data on which it is trained. and then we provide a sample of data to check the prediction made by the model is correct or not.

supervised learning can be grouped into 2 categories of algorithms
  • Regression
  • Classification

2) Unsupervised Learning

Unsupervised Learning is a type of learning in which a model learns without any supervision.

The machine learning model is only provided with independent variables, and not any dependent or labeled data to be trained and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into similar types of groups where each group has similar properties and is different from every other group.

Unsupervised learning can also be classified into 2 categories:
  • Clustering
  • Association

3) Reinforcement Learning

Reinforcement learning is a feedback-based learning method in which the learning agent receives a reward on each right prediction and gets a penalty(punishment) on each wrong punishment through which it learns and does not repeat the same mistake in the future. The agent learns automatically and this process goes on and it improves its performance.
In reinforcement learning agents interact with the environment and try to explore themselves to learn and improve performance. The goal of the agent is to get more and more reward points, and hence, improve performance.


Now, Machine learning has got a great advancement in its research and it is present everywhere around us and new applications are daily coming into the market. The field is so vast which carries ample opportunities for everyone to learn and have a great carrier with one's interest and visualization.

We have covered a brief introduction to machine learning with some of its applications as well we have seen how it came into the market with what scope and now we can visualize how it is disrupting the business decisions and creating an impact on society.

I hope that you all have a great start with Machine learning. Now in the coming article, we will cover each and every type in detail with complete mathematical intuition behind it. Till now, be calm, stay tuned, keep learning.

Further Reading


If you have any doubt or suggestions then, please let me know.

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