Category: Machine Learning Basics

Supervised learning and Unsupervised learning are two popular approaches in Machine Learning. The simplest way to distinguish between supervised and unsupervised learning is the type of training dataset and the way the models are trained. However, there are other differences, which are further discussed in the chapter. What is Supervised Learning? Supervised Learning is a machine…

What is Reinforcement Learning? Reinforcement learning is a machine learning approach where an agent (software entity) is trained to interpret the environment by performing actions and monitoring the results. For every good action, the agent gets positive feedback and for every bad action the agent gets negative feedback. It’s inspired by how animals learn from…

Semi-supervised learning is a type of machine learning that is neither fully supervised nor fully unsupervised. The semi-supervised learning algorithms basically fall between supervised and unsupervised learning methods. In semi-supervise learning, mahcine learning algorithms are trained on datasets that contains both labeled and unlabeled data. Semi-supervised learning is generally used when we have a huge…

What is Unsupervised Machine Learning? Unsupervised learning, also known as unsupervised machine learning, is a type of machine learning that learns patterns and structures within the data without human supervision. Unsupervised learning uses machine learning algorithms to analyze the data and discover underlying patterns within unlabeled data sets. Unlike supervised machine learning, unsupervised machine learning models…

What is Supervised Machine Learning? Supervised learning, also known as supervised machine learning, is a type of machine learning that trains the model using labeled datasets to predict outcomes. A Labeled dataset is one that consists of input data (features) along with corresponding output data (targets). The main objective of supervised learning algorithms is to learn an association…

There are various Machine Learning algorithms, techniques and methods that can be used to build models for solving real-life problems by using data. In this chapter, we are going to discuss such different kinds of methods. There are four main types of machine learning methods classified based on human supervision − In the next four chapters, we…

Data preparation is a critical step in the machine learning process, and can have a significant impact on the accuracy and effectiveness of the final model. It requires careful attention to detail and a thorough understanding of the data and the problem at hand. Let’s discuss how data should be prepared in order to fit…

While working with machine learning projects, usually we ignore two most important parts called mathematics and data. What makes data understanding a critical step in ML is its data driven approach. Our ML model will produce only as good or as bad results as the data we provided to it. Data understanding basically involves analyzing and exploring the…

Suppose if you want to start a ML project then what is the first and most important thing you would require? It is the data that we need to load for starting any of the ML project. In machine learning, data loading refers to the process of importing or reading data from external sources and…

What is Categorical Data? Categorical data in Machine Learning refers to data that consists of categories or labels, rather than numerical values. These categories may be nominal, meaning that there is no inherent order or ranking between them (e.g., color, gender), or ordinal, meaning that there is a natural ordering between the categories (e.g., education…