Types of Machine Learning : New Approach with Differences

Types of Machine Learning featured image

You guys are mostly familiar with the Trending word Machine Learning . Some of you also know the types of Machine Learning . So you must be wondering what value you will get in the article . See , We all know generally , There are 3 types of Machine Learning : Supervised , Unsupervised , reinforcement Learning . Some of us have also read about semi supervised learning as hybrid of supervised and unsupervised learning . You all are right buts its just a classification based on some set of parameters . There are few more sets of parameter which we use to classify machine learning . As a machine learning engineer , data scientist or AI engineer , I think you should know these basics . So article will open new cards on Types of Machine Learning : New Approach with Differences .


Types of Machine Learning –

There are three way to classify machine learning –

  1. Batch Machine learning Vs Online Machine Learning .
  2. Instance based Machine Learning and Model based Machine Learning .
  3. Supervised vs Unsupervised vs reinforcement Machine Learning .

Batch Machine learning Vs Online Machine Learning –

Batch Machine Learning is more popular with the name of offline Learning . This is not real time incremental learning . You need to train the Model with all available data at once  . In case you train and deploy the model , Now you have produced some more training data . You need to mix that new generated data with older one  and re train a new model . Now you may replace the old model with newer one . This retraining may require lot of training time and computing resources .But It ensure more hold on accuracy .

In the opposite side Online Machine Learning is a type of Machine Learning where we keep on the training the current model real time with new generated data . This involves less computation but there may garbage data enter into system and pull back the accuracy as well . there is a another name for this technique is out-of-core learning .

In General when we talk out Machine Learning , We focus on Batch Machine Learning . Hence most of the implementation ( code ) in popular machine learning algorithm frameworks contains the interface for Batch Machine Learning . Still Scikit-Learn Itself provide the interface for Online Machine Learning for below algorithms.-

  1. Feature extraction: Mini-batch dictionary learningIncremental PCA .
  2. Regression:     SGD Regressor,  Passive Aggressive regressor.

There are few more examples for but the motive here is to introduce you with the terminology only .

Instance based Machine Learning and Model based Machine Learning –

Instance based Machine Learning finds the similarity of new observation with existing data . It suggest the  same scenario of past with highest similarity score with current data . It is easy for you to relate with some real examples .  RBF networks ,  K-nearest neighbors algorithm are quite general and popular example for Instance based Machine Learning .

Model based Machine Learning is generalize kind of machine learning . Where we build the model using existing past data . Once we have the model , We predict using the machine learning model . Examples are decision tree , random forest , Logistic regression etc .


Supervised vs Unsupervised vs reinforcement Machine Learning –

In general When we categories Machine Learning , We get the name Supervised , Unsupervised and reinforcement learning . Supervised Machine Learning is a technique where training data contains the desire variable (target variable ) . In other words this learning has labeled training data . There are so many examples for supervised machine learning algorithms .Linear Regression ,Logistic Regression ,k-Nearest Neighbors ,Support Vector Machines ,Decision Trees and Random Forests and Neural networks are good examples of supervised machine learning algorithms .

Unsupervised Machine Learning is a technique where training data is not labeled . Actually Training data do not contains the outcome variable . k-Means , Expectation Maximization ,Hierarchical Cluster Analysis , Apriori , Eclat are good examples of Unsupervised Machine Learning .

Reinforcement Machine Learning is quite different than above two techniques . Here the learning system ( agent ) take some action and gives the reward for it . Reward can be negative or penalty for a wrong action . The agent keeps train it self and the generate its policy by rewarding . In the real world AI has quite evolving in this stream . Q-Learning , Asynchronous Actor-Critic Agents (A3C) , Temporal Difference (TD) , Monte-Carlo Tree Search (MCTS) are best example for reinforcement learning .

Now a Days a new kind of machine learning is also popular semi supervised learning . It a hybrid technique of Supervised and unsupervised machine learning .


Data Science Learner Team

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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages Datasciencelearner.com, where he and his team share knowledge and help others learn more about data science.
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