Explain Types of machine learning problems and Termilnologies if Machine Learning

Machine learning!!! So remember there are multiple ways to execute machine learning problems. Here Let's we discuss about few of those...........

• Supervised learning:
  
  The computer is presented with example inputs and their desired outputs, given by a “mentor”, and the goal is to learn a general rule that maps inputs to outputs. The training process continues until the model achieves the desired level of accuracy on the training data. few of the examples are:
  
  
  • Image Classification: You train with images/labels. Then in the future you give a new image expecting that the computer will recognize the new object.
    
    
  • Market Prediction/Regression: You train the computer with historical market data and ask the computer to predict the new price in the future.

• Unsupervised learning:
  
  No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It is used for clustering population in different groups. Unsupervised learning can be a goal in itself (discovering hidden patterns in data).
  
  
  • Clustering: You ask the computer to separate similar data into clusters, this is essential in research and science.
    
    
  • High Dimension Visualization: Use the computer to help us visualize high dimension data.
    
    
  • Generative Models: After a model captures the probability distribution of your input data, it will be able to generate more data. This can be very useful to make your classifier more robust.
    
    

• Semi-supervised learning:
  
  Problems where you have a large amount of input data and only some of the data is labeled, are called semi-supervised learning problems. These problems sit in between both supervised and unsupervised learning. For example, a photo archive where only some of the images are labeled, (e.g. dog, cat, person) and the majority are unlabeled.
  
  

• Reinforcement learning:
  
  A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

 

Terminologies of Machine Learning:

• Model
  A model is a specific representation learned from data by applying some machine learning algorithm. A model is also called hypothesis.
  
  
• Feature
  A feature is an individual measurable property of our data. A set of numeric features can be conveniently described by a feature vector. Feature vectors are fed as input to the model. For example, in order to predict a fruit, there may be features like color, smell, taste, etc.
  
  Note: Choosing informative, discriminating and independent features is a crucial step for effective algorithms. We generally employ a feature extractor to extract the relevant features from the raw data.
  
  
• Target (Label)
  A target variable or label is the value to be predicted by our model. For the fruit example discussed in the features section, the label with each set of input would be the name of the fruit like apple, orange, banana, etc.
  
  
• Training
  The idea is to give a set of inputs(features) and it’s expected outputs(labels), so after training, we will have a model (hypothesis) that will then map new data to one of the categories trained on.
  
  
• Prediction
  Once our model is ready, it can be fed a set of inputs to which it will provide a predicted output(label).