Machine Learning Homework Help

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Machine Learning

Machine learning is a subpart of artificial intelligence(AI) and computer science which emphasized on the use of data and algorithms to emulate the way that persons learn, actually focuses on improving its accuracy and integrity.

In other words we can say that the procedure or technique of data analysis that brutalize analytical model building is called machine learning. The data which is used in this model is called training data.

It is based on Artificial Intelligence(AI) which merge data with statistical tools to forecast an output or result which can be used to make actionable intuition.

Machine learning is a very significant tool that uses everything occur in the past to forecast the future. Today Machine learning is agitate in every industry and find the new revolution.

Example of Machine Learning:-  

A few actual life types of machine learning are as follows:-

Image-recognition . Image-recognition is actually a common as well as popular instance associated with machine learning within real life. Extremely famous and worldwide example of machine learning within real life is Image-recognition or image identification. It may  recognize an object like a digital icon, using the capability of the elements in black and white icons or even colour icons. Real life types of image-recognition: Label an x-ray as destructive or not.

Speech identification. Machine learning can convert speech into text. 

Medical discovery. 

Statistical profit. 

Guessing analytics. 


Types of machine learning:- Various kinds of machine learning algorithms that are the following: --

  • Supervised learning
  • Semi supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised Learning:

  • Makes machine learn explicitly
  • We take labelled data
  • Example:

         1) Hand written digit and character-recognition.

         2) Category associated with animal and objects based upon the actual input image

         3) Medical image categories diagnosis as well as numerous dieses .. and so on

Semi supervised Learning:

  • It's a mixture of supervised-learning as well as unsupervised-learning
  • It has each labelled and unlabelled data


Unsupervised Learning:

  • It is trained with unlabelled data
  • Example: 

         1) google news

         2) social network analysis

         3) market segmentation

Reinforcement Learning:

  • It is to identify the best policy or method that helps business in acheiving goals faster
  • Example: 

         1) Navigation

         2) Rebotics

         3) Gaming


Difference between the machine learning(ML) and artificial intelligence(AI):

- A form of analytic in which   software programs learn about data and find patterns or insights.
- Uses a variety of algorithms   and analytical models to support different types of data analysis
- Includes a combination of   supervised, unsupervised and reinforcement learning method
- Development of computerized   applications that simulate human intelligence and interaction.
- Handles specific tasks now,   general intelligence and cognitive capabilities are a future goal
- uses algorithms for machine   learning, natural language processing, automation and more


Application of machine learning:

  1. Medical diagnosis: While diagnosing a disease based on lab experiment, test results, DNA result etc
  2. Computer vision: Where the objects appear in an image
  3. Speech Recognition: Conversion of speech to text (e.g. google assistant)
  4. Image Recognition: Image recognition is one of the most commonly used applications of machine learning. ML is also used for image recognition
  5. Financial: In stock market, finance etc
  6. Weather forecasting
  7. Product counsel
  8. Traffic forecast
  9. Effective Personal Assistant
  10. Self-driving cars
  11. Online Fraud Observation
  12. Email Spam and viruses or bugs Filtering

Goal of Machine learning:-

The primary objective of machine learning should be to grouped closely with the goal of Artificial Intelligence(AI), to attain a detailed understanding about the nature of learning procedure (both human learning and other types of learning), about the calculation attributes of learning behaviors, and to place the learning ability in computer systems.

Future of Machine learning:-

Machine learning options pursue to integrate modifications in to businesses' primary procedures and therefore are getting much more regular within our every day life. This world wide machine learning market usually considers to raise by $8.43 billion in 2019 to $117.19 billion by 2027. So we can say that the future of machine learning is very broad and grow very rapidly.

Advantages of Machine Learning:-

Computerization of Everything (No mortal interruption required)

  • Machine Learning is liable for trimming the workload and time needed
  • Broad Range of Applications. 
  • Opportunity of Improvement.
  • Efficient control of Data. ...
  • Best for Education and Online Shopping. …
  • Authentic

Disadvantages of machine learning: -

  1. Risk or chances of High Error.
  2. Algorithm Selection is very difficult for different criteria. So this is very much time-consuming process.
  3. Data Acquisition - This process can sometimes produce unreliable data or we can say data inconsistency.
  4. Time and Space - This may sometimes cause the utilization of more CPU power and is very much time-taking process.

The advantages and advantages of this tool tell you the exact details of it. It is very necessary to know the benefits and loses of Machine Learning because it will help you in many ways like creating algorithm, making decision, etc.

Machine learning is a subset or subcategory of artificial intelligence. Instead of based on direct programming, it is a mechanism through which computers use a enormous set of data and apply algorithms to "train" on--to teach themselves -and built forecast.

Issues in machine learning:        

1) What algorithms are available for learning a concept. how will do they perform.

2) How much training data is efficient for learning a concept with high confidence 

3) When it is useful to use price knowledge

4) When it is useful to use prior knowledge

5) Are some training examples more useful than others.

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