Machine learning _ Machine learning python, Introduction, Examples, Pros & Cons and Conclusion

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Description

Machine learning has become a very important part of different research projects and commercial applications, but this job is not much exclusive to large organizations with large scale research teams. By using python, we can build our own machine learning python solutions.

What is Machine learning?

The method of data analysis that automates the systematical model building is called machine learning. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify figures and make decisions with minimum human mediation.

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Machine learning is a technique that has enhanced the quality of many professional and industrial processes as well as our daily lives. Machine learning is a subdivision of artificial intelligence (AI), which focuses on using statistics and data to build the intelligent computer systems to learn from specific databases.

Examples of Machine learning:

  1. Image recognition

Image recognition is one of the widespread and well-known examples of machine learning python in this era. It can recognize the objects as digital images, based on the strength of pixels in color images or black and white images.

Real-world example of the image recognition:

Let’s label an x-ray to know is it cancerous or not?

  • Assign a name to the snap face (aka “tagging” on the Google)
  • Recognize the handwriting by segregating a single letter into smaller images.

Machine learning python is regularly used for image recognition within the picture. By using the data of people, machine learning system can recognize the similarities and match their faces. This is used in law of enforcement.

  1. Speech recognition

Machine learning system can translate the speech of the person into text. Some software applications can change recorded speeches into the text files. These speeches can be segregated by powers on the time-frequency bands as well.

Real-world example of the speech recognition:

  • Voice- search
  • Voice- dialing
  • Appliance- control

Some devices such as Google Home and Amazon Alexa are the most common uses of speech recognition.

  1. Medical diagnosis

Machine learning may help with the diagnosis of many fatal diseases. ChatBots is used by many physicians with speech recognition capabilities to disclose patterns in the symptoms.

Real-world examples for medical diagnosis:

  • Assist in formulating the diagnosis of diseases or may recommends the treatment options
  • To recognize cancerous tissue, Oncology and pathology use machine learning system
  • Analyze body fluids

In the case of rare diseases, the use of image recognition software and machine learning helps scan patient images and identify phenol-types that may correlate with the rare genetic disease.

  1. Statistical arbitrage

Arbitrage is a self-operating dealing strategy which is used in finance to supervise a large size of securities. This technique is using the trading algorithms to analyze a set of securities using the economic correlations and variables.

 Real-world examples of statistical arbitrage:

Algorithm which,

  • Analyze the market micro structure
  • Analyze the large data sets
  • Identify arbitrage opportunities

Machine learning optimizes the arbitrage technique to increase the quality of results.

  1. Predictive analytics

Machine learning can classify the specific data into small groups and then define them by the rules sets by some analysts. When the classification is completed, the analysts will calculate the possibility of any error.

Real-world examples of predictive analytics:

  • Forecasting whether a transaction is fraud or legit
  • Improve forecasting systems to calculate the errors

Predictive analysis is one of the most important and promising examples of machine learning system. It’s applicable for everything; from product development to real estate pricing.

  1. Extraction

Machine learning with python can extract the structured data from unstructured information. Organizations gather the huge capacity of information from their customers. A machine learning algorithm drives the system of interpreting word processing files for the predictive analytics tools.

Definition of Machine learning python

Machine Learning python is a system, that analyze data quickly and learns to forecast the outcomes. It is making the computer system to learn from data and statistics. It is a step into the management of artificial intelligence (AI).

What can machine learning Python do?

Machine learning python is a type of artificial intelligence (AI) that provides computers with the ability to grasp without being explicit programmed. Machine learning python focuses on the growth of Computer programs that can be changed when reveal to newly formed data.

What projects can I do with machine learning?

  • Iris Classification
  • Movie Recommendations with Movielens Dataset.
  • Tensor Flow
  • Human Activity Recognition with Smartphones.
  • Wine Quality Predictions.
  • Breast Cancer Prediction.
  • Sales Forecasting with Walmart.
  • Stock Price Predictions

Advantages of Machine learning:

  1. Easily identifies trends and patterns

Machine Learning system can review large amount of data and find some specific patterns and trends that wouldn’t be appear to people. Machine learning uses the results to show the relevant ads to audience.

  1. Automation

With Machine learning, we don’t need to chaperone our project at every step. We gave machines the ability to learn, it make predictions and improve the algorithms on their own. Machine learning is good at recognizing the spam.

Disadvantages of Machine learning:

  1. Data Acquisition

Machine Learning requires the datasets to train, and these datasets are inclusive, unbiased, and of good quality.

  1. Time and Resources

Machine learning needs enough time to learn the basic algorithms and then develop enough to fulfill their purpose with a huge amount of relevancy and accuracy. It also needs some specific resources to function.

  1. Interpretation of Results

The major challenge of machine learning is the ability to accurately extract the results generated by the machine algorithms. Algorithms must be chosen carefully according to our requirements.

Conclusion:

Machine learning is quickly developing field in computer system. ML has applications in every other fields of study and It is already implemented, because machine learning can solve problems which are too difficult or time consuming for human brain to solve.

It is very useful for the development of this world but it also has some drawbacks like lack of time and resources, less interpretation of results and data acquisition.

 

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