Machine Learning is the study and application of mathematical algorithms and complex computers to automate the building of analytical models. It is part of artificial Intelligence. These machines can identify patterns in the data and make decisions, without needing to be programmed.
WHAT ARE MACHINE-LEARNING ALGORITHMS?
Machine learning is the use of algorithms to convert data into models. These models can be used to analyze data and make predictions. The type of algorithm used will determine the accuracy or performance of the model. Different algorithms work differently depending upon the problem statements and data.
Machine Learning Algorithms form the basis of machine learning. It is the algorithms that convert a dataset to a model. The type of algorithm used will determine which one. The problem statement and type of problem you are working with, as well as the available computing resources, will determine the results.
There are many algorithms out there today. Each algorithm can model a problem according to its interaction with the environment or the input data. They will all be covered in this article.
Based on the learning style of each individual, there are three types or machine learning algorithms.
Supervised Learning Algorithms
Supervised Learning algorithms use input data to provide answers.
The training process is used to prepare the model. It is meant to accurately predict data it hasn’t seen before, such as identifying a photograph of an animal that is included in the training data.
The model is trained until the model achieves the desired level in accuracy using the training data. An example of problems that can be solved by supervised-learning algorithms are Regression, Classification, Ensembling.
Logistic Regression, Linear Regression and Random Forest are some examples of example algorithms.
Un-Supervised Learning Methods
Un-Supervised Learning Algorithm does not label the input data. The algorithm simply goes through the data without knowing the result. However, they can be used to solve more complicated processing tasks than supervised algorithms.
Models are created by identifying patterns and hidden structures within the input data.
Unsupervised learning algorithms are able to solve many problems, including Dimensionality Reduction, Clustering, Association Rule Learning, and Dimensionality Reduction.
K-Means (Principal Component Analysis), Apriori Algorithm and PCA are examples of example algorithms.
Semi-Supervised Learning Methods
Semi-Supervised Learning Algorithms combines small amounts of labelled and unlabelled data in the training data.
Semi-supervised learning algorithms use the knowledge gained from a small set of labeled data point labels to label unlabeled data. The model must be able to recognize hidden patterns and organize data according to its predictions.
Semi-Supervised Learning Algorithms can be used to solve problems such as Regression or Classification.
These examples algorithms are often extensions of flexible methods that can be used to predict data not yet labeled.
Note: When working in the machine learning industry, you will most commonly use supervised or unsupervised learning algorithm when dealing with most of your business problem statements. Semi-supervised learning algorithms is useful when you are working with image processing tasks, such as image classifications and computer visualization, and when you have large datasets that only a few labels.
Machine Learning Algorithms
Machine Learning algorithms can be used in any way that is possible in computer science. The ML algorithms are described mainly using mathematics and pseudocode in research papers as well as textbooks.
An algorithm is an optimization process that minimizes model error in the training dataset.
These programs are able to learn and optimize their performance when new data is provided to them.
Multiple machine learning algorithms can be used in different cases. However, many ml practitioners employ standard machine learning algorithms to work on their projects.
Conclusion
Today’s discussion focuses on machine learning algorithms. It also discusses the types of machinelearning algorithms available and how they can be used to efficiently solve your problem.
Please leave a comment with any questions.