“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” ―Pedro Domingos

Have you heard people but only have a blurry idea of what that means? Are you tired of nodding your way through conversations with co-workers? Let’s change that!

What is Machine Learning ?

Machine learning is the science of creating algorithms and program which learn on their own. It is a type of Artificial Intelligence (AI) that provides computers the ability to learn without being explicitly programmed.In simple words, It is the same process by which a child learn’s to speak just in this case the Machine is the child and we can make the machine learn anything based on the previous experience or past data.

We all use products or services based on machine learning or in short ML in our day to day life such Google search engine, ad placement, stock trading, computer vision, drug design, Face Detection – Facebook photo tagging, Span email detection, Recommendation system by E-commerce giants such as Amazon and Ebay. Every tech company is making use of these ML Algorithm to provide a perfect user friendly experience and simultaneously multiply profits by increasing business.

ML is very similar to human learning .

Process involved in  Machine Learning

  1. Data Collection : Data is to be collected first ,it can be any in any form such as comma separated values(.csv), excel sheets, spread sheets, etc. (Note:It is not necessary in ML that the larger the data faster the learning.)
  2. Data Cleaning : Data is not always in its best form, It needs to be cleaned, manipulated and wrangled so that model can be applied easily.The True and False or the yeses and the noes are to be converted in binary form ie 0’s and 1’s.Exploratory analysis is one of the method of data clearing.
  3. Training a model : Suitable ML algorithm is applied to the data set and generally the data set is divided in two parts: Test and training sets. Algorithm is applied to the training data and checked on test data.This technique helps us to make proper and effective predictions.
  4. Testing a model : To evaluate if the model is precise and highly accurate we test the model on test data to check the precision and performance.
  5. Enhance performance : Error reduction and increasing accuracy by applying different model and on different variables of data .

Different types of ML Algorithm

Supervised Learning : It is also called as predictive model and is the most common type of learning.This type of learning is done on predefined rules.It is used when the output is definite.In simple words, it is used when the right type of training data is known ie labeled data.Examples:Nearest neighbour, Naïve Bayes, Decision Trees, Regression. etc.

 

 

Unsupervised LearningIt is also called as descriptive model and is used when no target is set .It is generally used to describe or generate pattern from the data.It is used when the output is indefinite.In simple words, it is used when the right type of training data is unknown ie Un-labeled data.Example:K-meansClustering Algorithm.

 

 

 

Semi-Supervised Learning : It is mixture of both supervised and un-supervised learning.It makes used of un-labeled and labeled data for training.These algorithms can perform better when we have a very small amount of labeled points and a large amount of un-labeled points used in modelling.

 

Reinforcement Learning : It is a type of unsupervised learning which allows the machine to automatically learn its behaviour from the previous output or feedback.The learning process keeps on adopting new results as time goes by. (Note : This method is too memory expensive to store every value.) Example problems : Self driving vehicles , Games such as Chess use this technique. Example algorithms : classification and regression.

Different Technique of Machine Learning

  • Regression Algorithms : Regression is a statistical process or predictive modelling technique which defines the relationship between dependent and independent variable .If one variable is used in regression then its is called simple regression if two or more than two variables are used then it is called as multiple regression.The most widely used regression algorithms  are :
    • Linear Regression
    • Logistic Regression
    • Polynomial Regression
    •   
  • Decision Trees Algorithms :It is a type of supervised learning algorithm that is mostly used for classification problems.It fits for both Categorical and continuous dependent variables.In this algorithm, we group the set of variables into two or more homogeneous sets. Decision Trees are often accurate and very favourite algorithm in machine learning .The most widely used Decision Tree algorithms are :

 

  • Conditional Decision Trees
  • Classification and Regression Tree.

 

Clustering Algorithm :Clustering is a method of unsupervised learning, which is used to form groups of similar data.In simple words, It is the process of organising objects into groups whose members are similar in  some way.The most widely used Clustering algorithms are :

    • K-means
    • K-medians
    • Hierarchical Clustering

 

  • Bayesian Algorithm:It is used for problems on classification and regression by applying Bayes theorem.It is easy to build and is usuallyused  for very large data sets.

    The most widely used Bayesian algorithms are :

      • Naive Bayes
      • Gaussian Naive Bayes

Applications of Machine Learning

Apart from general examples here are few  diverse example of Machine Learning listed below :

  • Speech recognition : Improvement in speech recognition is possible due to machine learning .Examples are Amazon Alexa, Google now, Apple Seri.
  • Anti-virus : Machine learning is used in Anti- Virus Softwares to improve the efficiency .
  • Genetics : Clustering algorithm in machine learning are used in genetics to help find gene associated with a particular disease.
  • Signal de-noising : Machine Learning algorithm such as K-means clustering  is used in video compression and de-noising techniques.
  • Anti-spam : machine learning is also used to train better anti-spam software systems.

Hence, In this article we tried to cover Machine Learning, how it works ,the types of learning algorithms ,some of the basic learning algorithms and some applications.I hope from now on, you just won’t be just nodding your way out of any Machine Learning related conversations. 🙂

If you found this article helpful please do share it.To know more about Machine learning and getting hands on knowledge about it go to Kasa Infotech Nagpur or you can also give them a call on +91-93250 13467.

Aniruddha Kalbande is young Engineer following his passion of entrepreneurship. Ureka360 is the 1st one among them which he started as a student.

1 COMMENT

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