Classical Machine learning consists of different phases such as modeling, evaluation and methods such as supervised and unsupervised learning. There are different techniques within the supervised and unsupervised learning which are presented in the next sections.
Classical Machine Learning
Machine Learning is related to a code which can learn by implicit code and logic. The input for the code is provided by the data for the training and learning purposes. Machine Learning is part of the computer science and related to Artificial Intelligence. The data is gathered, staged, and cleansed for training and learning purposes.
Real world has different workflows and procedures which can be modeled using mathematics. Machine Learning model is based on the mathematical model of the procedure. Learning is achieved by using the data provided. Data is collated from databases and devices. The data ingestion is done from different datasources.
Data is transformed, normalized, and cleansed before the data set is created for learning.Data is analyzed and patterns are identified for forecasting. Data set features are analyzed and identified for feature set creation.
Different sets of features from the data are used for selection of the approach. For example, for regression the complexity and the degree of the polynomial are the key factors. The model based on mathematics is chosen from a group of candidates. Most of the time, the simplest model is the best one for prediction and forecasting.
“We consider it a good principle to explain the phenomena by the simplest hypothesis possible”. – Ptolemy
Models can be selected from different approaches such as listed below:
- Support Vector Machine
- Logistic Regression
Machine Learning Algorithms are categorized into three types.
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
Before we look at different types of machine learning algorithms, let us look at the machine learning models, features and model creation, training and evaluation of the models in the next blog article.