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.
Let us look at some of the objectives under this Techniques of Machine Learning tutorial.
Ever wondered how Amazon makes recommendations?
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.
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.
The image below explains the relationship between input and output data of Supervised Learning.
Let’s look at the steps of Supervised Learning flow:
Given below are the steps for testing the algorithm 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.
Given below are 2 types of Supervised Learning.
Let us look at the classifications of Supervised learning.
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.
Given below are some elements of Regression Supervised learning.
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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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.
The 3 types of Unsupervised Learning are:
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.
Example: An online news portal segments articles into various categories like Business, Technology, Sports, etc.
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.
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.
It is a hybrid approach (combination of Supervised and Unsupervised Learning) with some labeled and some non-labeled data.
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.
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.
Some of the features of Reinforcement Learning are mentioned below.
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.
Lets us have a quick look at some important considerations in Machine learning below
Increasing a model’s complexity will reduce its bias and increase its variance.
Conversely, reducing a model’s complexity will increase its bias and reduce its variance. This is why it is called a tradeoff.
In Machine Learning, Representation refers to the way the data is presented. This often makes a huge difference in understanding.
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.
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.
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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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 |
Let us run through what you have covered in this tutorial of Machine Learning Techniques.
This concludes “Techniques of Machine Learning” tutorial. The next lesson is "Data Preprocessing”.
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Machine Learning | 27 Mar -1 May 2021, Weekend batch | Your City | View Details |
Machine Learning | 5 Apr -23 Apr 2021, Weekdays batch | San Francisco | View Details |
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