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[Tips] [Tech Class] Chapter 71: All About Machine Learning

2019-02-04 23:48:18
5808 122


Hello Mi Fans,
Welcome back to yet another Mi Community Tech Class Session. In the previous chapter, we dealt with Fiber Optics and in today's chapter, we will be learning more about Machine Learning and how do they help us in the real world.

What is Machine Learning?
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.


Types of machine learning problems
There are various ways to classify machine learning problems. Here, we discuss the most obvious ones.

1. On basis of the nature of the learning “signal” or “feedback” available to a learning system

Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, 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. Some real-life 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.

A simple diagram which clears the concept of supervised and unsupervised learning is shown below:



As you can see clearly, the data in supervised learning is labelled, whereas data in unsupervised learning is unlabelled.

  • Semi-supervised learning: Problems where you have a large amount of input data and only some of the data is labelled, 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 labelled, (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 with feedback in terms of rewards and punishments as it navigates its problem space.



2. On the basis of “output” desired from a machine-learned system

  • Classification: Inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are “spam” and “not spam”.
  • Regression: It is also a supervised learning problem, but the outputs are continuous rather than discrete. For example, predicting the stock prices using historical data.

An example of classification and regression on two different datasets is shown below:



  • Clustering: Here, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
    As you can see in the example below, the given dataset points have been divided into groups identifiable by the colours red, green and blue.



  • Density estimation: The task is to find the distribution of inputs in some space.
  • Dimensionality reduction: It simplifies inputs by mapping them into a lower-dimensional space. Topic modelling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.

On the basis of these machine learning tasks/problems, we have a number of algorithms which are used to accomplish these tasks. Some commonly used machine learning algorithms are Linear Regression, Logistic Regression, Decision Tree, SVM(Support vector machines), Naive Bayes, KNN(K nearest neighbours), K-Means, Random Forest, etc

Conclusion
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:



  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Does online recommendation offer such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? One of the more obvious, important uses in our world today.


Source 1, 2
In Case You Missed Previous Chapters:
Chapter 70: All About Fiber Optics
Chapter 69: All About S/PDIF

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2019-02-04 23:48:18
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well explained  
2019-02-05 02:08:45

Grandmaster Bunny

Solver13 | from Redmi Note 4

#2

Well explained!
2019-02-05 11:04:08

Grandmaster Bunny

prashanthsahu | from Redmi Note 4

#3

thank you for sharing such a awesome information .
2019-02-05 11:05:19

Techie Team

J C Paul | from Redmi Note 4

#4

Well explained!
2019-02-05 12:20:03

Advanced Bunny

1775780637 | from Redmi 5A

#5

thanks for explaination
2019-02-07 21:11:08

Master Bunny

Vidhu Bhushan | from Redmi 4A

#6

Nice. Thanks.
2019-02-07 23:42:30

Grandmaster Bunny

arun Ughade | from Redmi 4

#7

Well explained!.
2019-02-07 23:56:56

Grandmaster Bunny

arun Ughade | from Redmi 4

#8

Well explained!.
2019-02-07 23:57:42

Rookie Bunny

rahul77 | from Redmi Note 4

#9

nice explain
2019-02-08 00:02:07

Wizard Bunny

Sampath madurai | from Redmi Note 4

#10

Nice thanks for your information
2019-02-08 00:04:25
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