Machine Learning BASICS AND ITS TYPES and its difference
- 1. Trending Topics in Machine Learning
- 2. Machine Learning Algorithms Machine Learning uses algorithm to turn a dataset into model. Machine learning have the ability to calculate complex data in a fast manner. We can easily make decisions with less human intervention. Learning algorithms are classified into 3 categories: 1. Supervised Learning 2. Unsupervised Learning 3. Reinforcement Learning
- 3. Supervised Learning In supervised learning we use labelled datasets and model learns about each data . Then ,after training model is tested on the basis of test data . Supervised Learning is further divided into 2 types : ● Regression ● Classification
- 4. Regression It is used to determine the relationship between one input variable and output variable . It is used for prediction of continuous variables. Classification Classification is the process of grouping of objects and ideas into preset categories. Classification is a form of pattern recognition. One of the most common applications of classification is for filtering emails into “spam” or “non-spam”, used by email service providers.
- 5. Unsupervised Learning In unsupervised learning , we not used any labelled parameters and focus on discovering hidden structures from unlabeled data to help systems. Unsupervised learning further divideed into 2 categories: ● Clustering ● Association
- 6. Clustering Clustering ia collection of objects into clusters with similarities in one group and with no similarities in different group Association It is the method of determining the relationship between different variables in the database.
- 7. Reinforcement Learning Reinforcement Learning is different from supervised learning & unsupervised learning. We can say that it is a process of trial and error finally delivering results. In this learning agent automatically learns with experience without any label data.
- 8. Computer Vision Using computer vision technology we drive decisions using contextual image data.The main goal of computer vision technology user can easily interpret higher level concepts by the use of raw image.
- 9. Neural Networks Neural Network is also called deep neural network .It consists of at least 3 layers of neurons-input layer, hidden layer, output layer. Neural network is tool used in many different machine learning algorithms to process data input that machines can easily understand.
- 10. Predictive Learning Predictive analytics use techniques including ML, predictive modelling and data mining to predict future results. In predictive machine learning we analyze the historical data to find patterns and behaviours.
- 11. Data Mining Data mining helpful in extracting information from large amount of data for predicting the results on a certain data. The softwares used in data mining -clustering & segmentation software, classification, association, text analysis, statistical analysis.
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