What is machine learning?
Machine learning can be defined as the capacity of machines to emulate human intellectuality.Machine learning is a subset of artificial intelligence which work on the basis of mathematical models/algorithms.
How machine learning algorithms work?
The very first step is knowing the problem (identification of problem must be clear) and then data is collected; collected data must be a good representative of the desired problem in a real time manner.Collected data is then processed for its cleaning, error removal, transforming and encoding the data in an appropriate fitting format that the algorithm can understand.Machine learning models are trained on the basis of processed data. Important thing in machine learning algorithm is the selection of model as there are multiple types of machine learning models available.
The algorithm studies the data and detects pattern and relation between different parameters of data and modify the parameters to reduce the contrast between predicted and real results.Model training continues until the outcomes difference between actual and predicted results is significantly minimized.After model training, it is evaluated on a different data set where its performance is determined and helps in further improvement of the model.Once data is processed, the model has been trained and evaluated successfully then it can be deployed in predicting unknown data or new data through an application programming interface (API) or integrated into a large system.
Classification of machine learning algorithms.
Machine learning algorithms are classified into different types based on their learning approach and strategy.
Supervised learning.
The commonly used algorithm type is supervised learning. It can predict results on the basis of prior data.It further includes multiple algorithms such that linear and logistic regression, decision trees, support vector machines (SVM), random forests and neural networks.
Semi supervised learning.
In a very simple way, it can be explained as an amalgamation of supervised learning and unsupervised learning. The model is preprogrammed to bring the required result but it has to process data, learn and classify data by itself.
Unsupervised learning.
In this algorithm, data is not supervised and is left free to process without any required outcomes or results. It helps in finding hidden patterns in the data.Unsupervised learning algorithms further include clustering algorithms (k-means, hierarchical clustering), principal component analysis (PCA), and anomaly detection.
Reinforcement learning.
The goal of reinforcement earning is to change the outcomes from time to time depending on the environment and feedback. Machines learn with the rewards of reinforcement learning and correct their mistakes with penalties.Q learning and deep learning are commonly used reinforcement learning algorithms. Each of these approaches has its own unique characteristics and is suitable for different types of problems.Top of FormBottom of Form
Top most popular and common machine learning algorithms
Naive Bayes Classifier Algorithm (Supervised Learning – Classification)
K Means Clustering Algorithm (Unsupervised Learning – Clustering)
Support Vector Machine Algorithm (Supervised Learning – Classification)
Linear Regression (Supervised Learning/Regression)
Logistic Regression (Supervised learning – Classification)
Artificial Neural Networks (Reinforcement Learning)
Decision Trees (Supervised Learning – Classification/Regression)
Random Forests (Supervised Learning – Classification/Regression)
Nearest Neighbours (Supervised Learning)
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