Machine Learning
- Machine Learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and makes future predictions. It finds the pattern in the data , based on the pattern it gives the future predictions from the unseen data.
- It is a way to understand the data and find the patterns in that.
Machine Learning Application:
- Web Search Engine:
- One of the reasons why search engines like google, Bing etc. work so well is because the system has learnt how to rank pages through a complex learning algorithm.
- Photo tagging Applications:
- Facebook or any other photo tagging application, the ability to tag friends makes it even more happening. It is all possible because of a face recognition algorithm that runs behind the application.
- Spam Detector:
- Our mail agent like Gmail or Hotmail does a lot of hard work for us in classifying the mails and moving the spam mails to spam folder. This is again achieved by a spam classifier running in the back end of mail application.
Types of Machine Learning
Supervised Machine Learning
- An algorithm learns from example data and associated target responses that can consist of numeric values or string labels.
- Generally the algorithm should find the pattern how input and output is mapped
Two types of Supervised Learning:
- Regression:
- The problem is regression type when the output variable is real or continuous.
- Example :
- Predicting salary of person based on past experience of person.
- Predicting house price based on house details.
- Classification:
- A classification problem is when the output variable is a categorical.
- Example:
- Predicting incoming mail is spam or not spam.
- predicting whether loan should be accept or reject based on credit score of incoming loan application.
- Algorithms:
- Linear Regression
- Logistic Regression
- k-Nearest Neighbor
- Support vector Machine
- Decision Tress
- Ensemble Learning
- Naive Bayes Algorithm
Unsupervised Machine Learning
- ML learns from unlabeled data set then it is unsupervised ML.
- The data set contain only inputs and no associated target responses.
- Algorithms:
- Clustering Algorithms
- Association clustering
Reinforcement Learning
- Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.
Terminologies used in 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.
- For example, in order to predict a fruit, there may be features like color, smell, taste, etc.
- 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).
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