Learn About Machine Learning Basics

Machine Learning

Machine Learning is a booming research area across the globe. Advances in Machine Learning will make a big difference in many fields in the future. This is a vast area in which many types of algorithms are embedded. This article will help you get started with Machine Learning.

First, let us discuss what Machine Learning is.

Machine Learning is the science of getting computers to act without being explicitly programmed.

The main difference between a human and a computer is - Humans learn from past experience while computers need to be told what to do. Computers need to be programmed so that they follow instructions.
 
Making computers learn from past experience is called Machine Learning. For computers, past experience is nothing but data.

ML can be implemented with the help of algorithms. The machine learning algorithm is designed in such a way that it can predict some results based on inputs given. The more data we are going to give, the more accurate the prediction/ decision will be.

The algorithms can work and process an infinite amount of data which human brains cannot do.

With the advent of so-called ‘Big Data’, there are very large/ huge data sets available which are required for Machine Learning to be successful.

In one way, we can say that Machine Learning is similar to Data Mining. The base core of ML is dependent on Mathematics. It actually relates to statistical analysis and iterative learning.

Let us see a few real-time applications of Machine Learning so as to get a better idea.

  • Detecting Spam emails.
  • Web Search and Recommendation Engines.
  • Image and Speech Recognition.
  • Medical Diagnosis.

Machine Learning Techniques

There are three techniques in Machine Learning. These techniques are used to build a Data Model which helps stakeholders make decisions related to business.

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Let us look into these Techniques.

Supervised Learning

It is the most commonly used Technique among the three. In this kind of learning, the algorithm uses training data to find a link between input and output. This builds a model based on given inputs and output and gives a reasonable prediction for the new inputs. Here the training dataset is used to train the machine. This model is often used for systems like image recognition, speech recognition.

Types of algorithms which come under supervised learning are

  • Classification and
  • Regression

Classification

This model classifies the input data into categories.

  • Image Classification

Regression

A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used, the simplest is the linear regression.

Unsupervised Learning

These algorithms are used to find hidden patterns in the given data. In this algorithm, we do not have any target/ output variable which helps in predicting the result. They are called unsupervised because they are left on their own to analyze the data given and find some specific structure or pattern. Here, the machine learns through observation. ­­­

Types of algorithms which come under unsupervised learning are

  • Clustering
  • Association Rules

Clustering

Clustering tries to group a set of objects and find whether there is some relationship between the objects.

Association Rules (Pattern Detection)

It is a rule-based machine learning for discovering interesting relationships between variables in large databases.

Reinforcement Learning

This is a type of Machine Learning which tells the computer if it has made the correct decision or not. With enough iterations, it will eventually be able to predict the correct outcomes.

List of common algorithms which are widely used in Machine Learning

The algorithms play a major role in Machine Learning.

  • Linear Regression
  • Naive Bayes Algorithms
  • Decision Tree Algorithms
  • Gradient Descent
  • Neural Network
  • Deep Learning Algorithms
  • K-Means
  • KNN (K – Nearest Neighbors)
  • Q Learning
  • SVM (Support Vector Machine)

Algorithm Selection

It is very difficult to say which algorithm works best for the given problem. Sometimes just a trial and error method is followed. But to some extent, it can be decided based on the type and size of the data we are working with. We can go for machine learning when we have a complex task involving a large amount of data.

Opportunities

Giant companies like Google, Apple, Microsoft, Amazon are investing in ML as it is one of the booming research areas in many fields. So in the coming years, we will have many opportunities in Machine Learning. So it is better to learn to design algorithms by using either Python or R language.

Languages used to design an Algorithm for Machine Learning

There are many languages which are used to implement Machine Learning out of which two are widely used.

  • Python
    Python is one of the best ways to getting started with Machine Learning as it is easy to learn and easy to read. It has some good Machine Learning libraries such as scikit-learn, pyML, and pybrain.
  • R
    It is open source statistical programming language. The syntax is not easy to learn, but it is worth learning as it also has good Machine Learning Packages and Visualizations tools.

All the best to all who want to work in this area to achieve your goals.

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