Techniques of Machine Learning

This is the ‘Techniques of Machine Learning’ tutorial, which is a part of the Machine Learning course offered by Simplilearn. We will learn various Machine Learning techniques like Supervised Learning, Unsupervised Learning, Reinforcement Learning, Representation Learning and a few others.

Objectives

Let us look at some of the objectives under this Techniques of Machine Learning tutorial.

  • Explain unsupervised learning with examples
  • Describe semi-supervised learning and reinforcement learning
  • Discuss supervised learning with examples
  • Define some important models and techniques in Machine Learning

Supervised Learning: Case Study

Ever wondered how Amazon makes recommendations?

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Amazon uses supervised learning algorithms to predict what items the user may like based on the purchase history of similar classes of users. New Input Predicted Output Algorithm Trained on Historical Data.

What is Supervised Learning?

Supervised Learning is a type of Machine Learning used to learn models from labeled training data. It allows us to predict the output for future or unseen data.

Understanding the Algorithm of Supervised Learning

The image below explains the relationship between input and output data of Supervised Learning.

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Supervised Learning Flow

Let’s look at the steps of Supervised Learning flow:

  • Data Preparation
  • Training Step
  • Evaluation or Test Step
  • Production Deployment

Testing the Algorithm

Given below are the steps for testing the algorithm of Supervised Learning.

  1. Once the algorithm is trained, test it with test data (a set of data instances that do not appear in the training set).
  2. A well-trained algorithm can predict well for new test data.
  3. If the learning is poor, we have an underfit situation. The algorithm will not work well on test data. Retraining may be needed to find a better fit.
  4. If learning on training data is too intensive, it may lead to overfitting – a situation where the algorithm is not able to handle new testing data that it has not seen before. The technique to keep data generic is called regularization.

Examples of Supervised Learning

Take a quick look at some examples of Supervised Learning that are given below.

Example 1: Voice Assistants like Apple Siri, Amazon Alexa, Microsoft Cortana, and Google Assistant are trained to understand human speech and intent. Based on human interactions, these chatbots take appropriate action.

Example 2: Gmail filters a new email into Inbox (normal) or Junk folder (Spam) based on past information about what you consider spam.

Example 3: The predictions made by weather apps at a given time are based on some prior knowledge and analysis of how the weather has been over a period of time for a particular place.

Types of Supervised Learning

Given below are 2 types of Supervised Learning.

  • Classification
  • Regression

Classification Supervised Learning

Let us look at the classifications of Supervised learning.

  • Answers “What class?”
  • Applied when the output has finite and discrete values

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Example: Social media sentiment analysis has three potential outcomes, positive, negative, or neutral.

Example: Given the age and salary of consumers, predict whether they will be interested in purchasing a house. You can perform this in your lab environment with the dataset available in the LMS.

Regression Supervised Learning

Given below are some elements of Regression Supervised learning.

  • Answers “How much?”
  • Applied when the output is a continuous number
  • A simple regression algorithm: y = wx + b. Example: the relationship between environmental temperature (y) and humidity levels (x)

Example

Given the details of the area a house is located, predict the prices. You can perform this in your lab environment with the dataset available in the LMS.

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Unsupervised Learning: Case Study

Ever wondered how NASA discovers a new heavenly body and identifies that it is different from a previously known astronomical object? It has no knowledge of these new bodies but classifies them into proper categories.

NASA uses unsupervised learning to create clusters of heavenly bodies, with each cluster containing objects of a similar nature. Unsupervised Learning is a subset of Machine Learning used to extract inferences from datasets that consist of input data without labeled responses.

Types of Unsupervised Learning

The 3 types of Unsupervised Learning are:

  1. Clustering
  2. Visualization Algorithms
  3. Anomaly Detection

The most common unsupervised learning method is cluster analysis. It is used to find data clusters so that each cluster has the most closely matched data.

Clustering

Example: An online news portal segments articles into various categories like Business, Technology, Sports, etc.

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Visualization Algorithms

Visualization algorithms are unsupervised learning algorithms that accept unlabeled data and display this data in an intuitive 2D or 3D format. The data is separated into somewhat clear clusters to aid understanding.

In the figure, the animals are rather well separated from vehicles. Horses are close to deer but far from birds, and so on.

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Anomaly Detection

This algorithm detects anomalies in data without any prior training. It can detect suspicious credit card transactions and differentiate a criminal from a set of people.

What is Semi-Supervised Learning?

It is a hybrid approach (combination of Supervised and Unsupervised Learning) with some labeled and some non-labeled data.

Example of Semi-Supervised Learning

Google Photos automatically detects the same person in multiple photos from a vacation trip (clustering – unsupervised). One has to just name the person once (supervised), and the name tag gets attached to that person in all the photos.

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What is Reinforcement Learning?

Reinforcement Learning is a type of Machine Learning that allows the learning system to observe the environment and learn the ideal behavior based on trying to maximize some notion of cumulative reward.

Features of Reinforcement Learning

Some of the features of Reinforcement Learning are mentioned below.

  • The learning system (agent) observes the environment, selects and takes certain actions, and gets rewards in return (or penalties in certain cases).
  • The agent learns the strategy or policy (choice of actions) that maximizes its rewards over time.

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Example of Reinforcement Learning

In a manufacturing unit, a robot uses deep reinforcement learning to identify a device from one box and put it in a container. The robot learns this by means of a rewards-based learning system, which incentivizes it for the right action.

Important Considerations in Machine Learning

Lets us have a quick look at some important considerations in Machine learning below

Bias and Variance Tradeoff

  • Bias refers to the error in the machine learning model due to wrong assumptions. A high-bias model will underfit the training data.
  • Variance refers to problems caused due to overfitting. This is a result of the over-sensitivity of the model to small variations in the training data. A model with many degrees of freedom (such as a high-degree polynomial model) is likely to have high variance and thus overfit the training data.

Bias and Variance Dependencies

Increasing a model’s complexity will reduce its bias and increase its variance.

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Conversely, reducing a model’s complexity will increase its bias and reduce its variance. This is why it is called a tradeoff.

What is Representation Learning?

In Machine Learning, Representation refers to the way the data is presented. This often makes a huge difference in understanding.

Example of Representation Learning

The figure shows sample data in Cartesian coordinates and polar coordinates. In this particular case, categorization becomes easier when data is presented in a different coordinate system. Hence, representation matters.

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Linearly Separable or Not

The convergence of the learning algorithms (like perceptron) is only guaranteed if the two classes are linearly separable and the learning rate is sufficiently small.

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If the two classes can't be separated by a linear decision boundary, you can set a maximum number of passes over the training dataset (epochs) and/or a threshold for the number of tolerated misclassifications. The perceptron would never stop updating the weights otherwise.

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Other Machine Learning Techniques

Let us look at some of the other Machine Learning Techniques below.

Techniques Working

Probabilistic Models

Model the probability distribution of a data set and use it to predict future outcomes

Decision Trees

Arrive at a hierarchical decision tree structure

Clustering

Classify data based on closest data points appearing in the same cluster

Associated Rules

A method to discover what items tend to occur together in a sample space

Deep Learning

Based on Artificial Neural Network models

Key Takeaways

Let us run through what you have covered in this tutorial of Machine Learning Techniques.

  • Supervised Learning is a type of Machine Learning used to learn models from labeled training data. It allows us to predict the output for future or unseen data. Two major types of Supervised Learning are Regression and Classification.
  • The ML process (for supervised learning) entails data pre-processing, training the model, and testing the trained model and production deployment. If the training is poor, it may lead to underfitting (model does not satisfy the test data).
  • If the training is too intensive, it may lead to overfitting (the model is not able to handle new unseen test data).
  • Unsupervised Learning is a subset of Machine Learning used to extract inferences from datasets that consist of input data without labeled responses. Some examples of Unsupervised Learning include Clustering and Visualization algorithms.

Conclusion

This concludes “Techniques of Machine Learning” tutorial. The next lesson is "Data Preprocessing”.

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