Machine Learning and its Algorithms

Created date : 27-07-2020

Author Name : Annamalai

MachineLearning :


Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of

computer programs that can access data and use it learn for themselves.


What is machine learning with example?

Top 10 real-life examples of Machine Learning. ... For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.


What is machine learning good for?


Machine learning is taking over the world - it is benefiting companies across industries. It is helping organisations create systems that can understand, learn, predict, adapt and operate on their own. Thus, understanding how machine learning works is one of the most valuable and useful things you can do.


What are the different types of machine learning?


These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.


Is machine learning hard?


There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application.


What are the basics of machine learning?


Every machine learning algorithm has three components: Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others. Evaluation: the way to evaluate candidate programs (hypotheses).


How is machine learning used?


Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more.


Is machine learning good or bad?

Poor data quality is enemy number one to the widespread, profitable use of machine learning. The quality demands of machine learning are steep, and bad data can rear its ugly head twice both in the historical data used to train the predictive model and in the new data used by that model to make future decisions.



8 problems that can be easily solved by Machine Learning:

  • Manual data entry

  • Detecting Spam

  • Product recommendation

  • Medical Diagnosis

  • Customer segmentation and Lifetime value prediction

  • Financial analysis

  • Predictive maintenance

  • Image recognition (Computer Vision)

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