How to start with Machine Learning

How to start with Machine Learning

The title itself would have made you feel like some cyborg stuff. The term machine learning literally means, programming the particular machine in such a way, so that it is able to perform operations and programs on its own, without human programming. This is a fabulous idea, as the aspect of machines being able to do trained things on their own without a human hand would lead to increased efficiency. But in order to understand how to star with machine learning, you must understand what exactly is machine learning.


In technical terms, machine learning is teaching the computer to make decisions and predictions based on the data fed into it. This is taken place by means of statistics and algorithms. This is achieved with the help of Artificial Intelligence (AI). This is possible due to the memory that is stored into the computer by continuous training that is given to the computer by feeding in sample data and programming the computer. The most famous application of machine learning is the use of them in search engines which predict the website you require by scanning the library for the keywords searched by the user.


In order to be an expert in machine learning, one doesn’t need to be an engineer or a PhD, the thorough knowledge about the important subjects required for machine learning as well as its practical knowledge will be more than sufficient. In this article we have provided all the steps regarding how to start with machine learning in a detailed manner as follows:

1) The first brick that lays the foundation: This is the most basic, but important step in machine learning. There are certain fundamentals that need to be learned and practised before jumping towards machine learning. Machine learning is a complex study which requires you to have knowledge in Python, statistics and maths. The knowledge regarding the above 3 aspects is very essentials when moving forward towards learning machine learning as all 3 play a very important role. Python being essential for the purpose of programming, statistics in order to understand the assembly of data yourself and maths, in order to understand algorithm. And it goes without saying, that a thorough knowledge in computers is also expected.

2) The pillars that hold on to the structure: After being able to match the criterion, the next step involves studying all the basic terms and concepts of machine learning. This enables you to gain deeper understanding into the notion of Machine Learning. The theory behind machine learning and data science is essential in order to shape the entire purpose.

In this step, there are various concepts and terms that one should learn, in order to concur the theoretical counterpart, since theoretical application is as important as practical application. They both can’t go with the other. Theory might sound boring in an excited concept as such, but the importance of the theory of machine learning might become very interesting once you realise the importance of it. There are many basic elements to learn such as

  • Data collection, integration, cleaning and processing
  • Algorithms involved in machine learning
  • Supervised machine learning
  • Unsupervised machine learning
  • Artificial Intelligence
  • Neural Network or Artificial Neural Network (ANN)
  • Back-propagation
  • Deep Learning or Deep Neural Network (DNN)
  • Linear regression
  • Logistic regression
  • K-Nearest Neighbours (K-NN)
  • Random Forest
  • Ensemble Learning
  • Gradient boosted decision trees
  • Overfitting
  • Underfitting
  • Regularization
  • L1 vs L2 regularisation
  • Cross validation
  • Performance metrics for regression
  • Performance metrics for classification problems

All the above-mentioned topics are a part of the Machine Learning Theory.

3) Practical application: This is the most fun part of the entire learning process, as it lets you to apply the theoretical knowledge in the practical field. This can be achieved by constantly taking part in projects and competitions via constant practise. The number of times you practise your knowledge would reduce the number of mistakes and fill the holes which might be left even after thorough theoretical knowledge.

Thus, it is composed of the prerequisites, theory and practical applications, all of which being essential for the course. Machine Learning is therefore based solely on your interest and dedication just like every other task in life.

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