Introduction to Machine learning

Machine learning is a form of Artificial Intelligence (AI) that allows computer systems able to study without being explicitly programmed. It is concerned with developing software that improves from past experience, used in programs for classification and prediction. Predictions are made on the basis of visual data and measurement data. This extraction of data is done by machines through statistical analysis.
Machine learning focuses on the development of computer applications that may exchange while exposed to new information. It can be split into three categories: Supervised learning (algorithms are trained using
labeled examples where desired output is known), Unsupervised learning (used against data that has no historical labels) and Reinforcement learning (uses trial and error actions which yield the greatest rewards).

Benefits of Machine Learning: After development of algorithms in machine learning there are some advantages of machine learning to notice:

  • Model has become more relevant due to the iteration process which delivers the higher level of accuracy which allow us to find the best fit data.
  • It automatically apply complex mathematical calculations over and over, faster and faster which gives better results and automatically apply those results to decision making and actions.
  • Allow high-value predictions that provide better decisions and smart actions in real time without human intervention.

Application of Machine Learning: Machine learning is a way of tuning a system with tunable parameters, some applications are:

  • Machine learning can be used to improve applications such as Face detection, Face recognition,Image classification, Speech recognition.
  • Machine learning is used in weather forecasting software to give quality information of the weather forecasting.
  • Machine learning algorithms are being used in lots of novel and interesting ways. It’s becoming increasingly important for companies to harness the power of their data and use it to make smart decisions.

Main Steps for learning toolbox are:

  • Getting Started: Learn the basics of Statistics and Machine Learning Toolbox.
  • Descriptive Statistics and Visualization: Import and export of Data, Visualization and description.
  • Probability Distribution: Parameter generation, Data Frequency models and Random Sample Generation.
  • Hypothesis Tests: It includes t-test, F-test, chi-square goodness-of-fit test, and more.
  • Supervised Learning: It includes classification, Linear Regression, KNN, NN classifier.
  • Unsupervised Learning: It includes clustering analysis, Anova and Regression algorithms.
  • Dimensionality Reduction: It includes non-negative matrix factorization, factor analysis, sequential feature selection and PCA.
  • Statistics of Industrial: survival and reliability analysis, Design of experiments (DOE) statistical process control.

Summary:

Machine Learning can be applied to complex research fields such as quality improvement and its approaches are of particular interest considering steadily increasing search outputs and accessibility of the existing evidence is a particular challenge of the research field in quality improvement. Machine Learning allow improved predictive performance.

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