What is Machine Learning?

The name ” Machine Learning ” was started in 1959 by “Arthur Samuel”.
Artificial intelligence is a very popular think in the world so Machine Learning is a part of Artificial Intelligence (AI). Some times we can say “MachineLearning is a branch of Artificial Intelligence”. That gives system the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning is using for development of computer programs that can access data and use it learn for themselves.
The main aim of Machine Learning earning is to create intelligent machines which can think and work as human beings. Machines can learn automatically from the experience.

Requirements of creating good machine learning systems.

  1. Data – Input data is requiring for predicting the output.
  2. Algorithms – Machine Learning is dependent on algorithms. statistical algorithms to determine data patterns.
  3. Automation- It is ability to make systems operate automatically.
  4. Iteration     – The complete process is iterative repetition of process.
  5. Scalability  – The capability of the machine can increased or decreased in size and scale.

Type of Machine Learning 

  • Supervised Learning
  • There are two types of supervised learning  techniques:
  • (1) Regression                                              
  • (2) Classification                  
  • In this method, input and output is provided to the computer along with feedback during the training. The main goal of this training is to make computer learn how to map  input to the output.                                           
  • Unsupervised Learning
  • It help us to finds all kinds of unknown patterns in data. In this case, no such training. It find the output on its own. Unsupervised Learning is mostly appliede  on transactional data. It is using in more complex tasks. It is also known as self-organization.  It uses another approach of iteration known as deep learning to arrive at some conclusions.                                                          
  • Ex – Cluster Analysis
  • Reinforcement Learning.                                                                                        
  • This type of learning uses three things, such as,
  • agent environment action  An agent is the one that perceives its surroundings, an environment. The main aim in reinforcement learning is to find the best possible policy.

Examples 

Machines are using past data for given future outcomes. The intelligent systems built on machine learning algorithms. ML application provide results on the basis of past experience.
  • Speech Recognition
  • spoken words translate into the text. It is also know as computer speech recognition or automatic speech recognition. This software application can recognize the words spoken in an audio clip or file and then subsequently convert the audio into a text file. Speech Recognition is using in the application like voice user interface, voice searches and more. Voice user interface include call routing and voice dialing, data entry and the preparation of structured documents.
  • Image Recognition    
  • Image Recognition is one of the common uses of machine learning. There are many situations where you can classify the objects as a digital image.  Examples -: In the case of a black and white image, the intensity of each pixel is serving as one of the measurements. In colored images, each pixel provides 3 measurements of intensities in three different colors – red, green and blue (RGB).
  • Medical Diagnosis    
  • Machine Learning can be used in the techniques and tools that can help in the diagnosis of diseases. It is using for the analysis of the clinical parameters and their combination for the prognosis example prediction of disease progression for the extraction of medical knowledge for the outcome research, for therapy planning and patient monitoring. These are the successful implementation of the Machine Learning methods.                                            
  • Financial Services
  • ML has a lot of potential in this financial and banking sector. It is the driving force behind the popularity of financial services. It can help the banks,  financial institutions to make smart decisions. It can also perform the market analysis. Smart machines can be training to track the spending patterns.  It can identify easily and react in real-time.       
  • Learning associations
  • One of the applications of machine learning is studying the associations between the products that people buy. If a person buys a product, he is showing similar products because there is a relation between the two products. When any new products are launching in the market, they are associating with the old ones to increase their sales.                                                                        
  • Classification                                                                                                         
  • Classification helps to analyze the measurements of an object to identify the category to which that object belongs. For example, before a bank decides to distribute loans, it assesses the customers on their ability to pay loans. 

Benefits of Machine Learning

Everything is dependent on machine learning. So now we find it.
  • Decision making is faster.                                                                 
  •   ML provides the best possible outcomes by prioritizing the routine decision-making processes.
  • Innovation
  • ML uses advanced algorithms that improve the overall decision-making capacity.
  • Adaptability                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ML provides the ability to adapt to anew changing environment rapidly. It always move with environment changes. Data is update always.
  • Business growth
  • It is a valuable benefit of Machine Learning. Overall business process and workflow will be faster and hence with machine learning.
  • Good Outcome
  • With machine learning the quality of the outcome is improving with lesser chances of error.

Disadvantages of Machine Learning

  1. Data Acquisition                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ML requires massive data sets to train on, and these should be inclusive/unbiased, and of good quality.                                       
  2. Time and Resources                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          MLneeds enough time to learn and develop. It also needs massive resources to function. This can mean additional requirements of computer power for you.

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