Introduction to MACHINE LEARNING – Techienest

Introduction to MACHINE LEARNING

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Machine Learning is a branch of Artificial intelligence or we can say it as an application of AI. Machine Learning provides the artificial system the ability to learn and improve form the past experiences of humans without the explicit programming. Machine Learning mainly focus on the development of the computer programs that can access the data and use the data to learn for themselves.
The primary aim of machine learning is to allow the computer system to learn automatically or by their own without the human assistance and adjust the actions accordingly.


Applications of Machine Learning

Some of the Applications of Machine Learning are:

1. Recognition of image:
Image Recognition is one of the most common uses of Machine Learning. In all the situations we can classify the object as the digital image. In the digital images in the output, the measurement of each pixel is taken into consideration.

2. Speech Recognition:
Speech recognition is a technique of translating of words that are spoken to text. In speech recognition, a software is used which recognize the words that are spoken. This application measures the speech as a set of numbers that represents the signal of the speech. The signals are divided into portions that have distinct words. In each segment, the signal of the speech is represented by the energy in different time frequency.

3. Statistical Assistance:
In Finance, Statistical assistance refers to automated trading that is typical 2for a short-term and involves a large number of securities. In this, users try to implement the trading algorithms for a set of securities on basis of quantities. In the case of classification, the categories might be sold, buy or do nothing for each security. In case of estimation, one might try to predict the future result of the finance or the expected returns on each security.

4. Classification:
Classification is the process of placing an individual under the several studies. Classification helps analysts to use measurements of objects to identify the category to which the object belongs and to establish a rule and use the data.
For example, before the bank decides to pay off the loan, it assesses customer on their ability to repay the loan back.

5. Prediction:
In this first, the system tries to classify the data into certain groups. Once the classification is done, as per need it can compute the probability. With the help of these computations, the system predicts the results.

6. Extraction:
Extraction is another application of machine learning. It is the process of extracting information from unstructured data. Like, web pages, blogs, reports and emails. The process of extraction takes inputs as a set of documents to summarized output.


Companies Working on Machine Learning

1. Google:
Google is working on and developing a photographic memory.
2. Baidu:
Baidu is working on Accurate speech Recognition with GPU-Accelerated Deep Learning
Pinterest is building distributed systems using machine learning to accelerate the work of discovery.


Prerequisites for learning Machine Learning:

Machine Learning needs some pre-requisites
a. Mathematics
b. Basic Maths
c. Linear Algebra
d. Probability & Statistics
e. Calculus
f. Programming
g. Python or R Programming


Future of Machine Learning:

Machine Learning is currently one of the hottest topics in IT. The reason stems from the seemingly unlimited use case where ML can play from false detection to self-driving Cars and even the price prediction.
The Areas where Machine Learning will play an important role is:
1. Quantum computing
2. Better Unsupervised Algorithms
3. Collaborative Learning
4. Deep Personalization

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