Category: Machine Learning Basics

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…

Machine learning models are only as good as the data they are trained on. Therefore, obtaining good quality and relevant datasets is a critical step in the machine learning process. There are many open-source repositories, like Kaggle, from where you can download datasets. You can even purchase data, scrap a website, or collect data independently.…

In the world of artificial intelligence, two terms that are often used interchangeably are machine learning and deep learning. While both of these technologies are used to create intelligent systems, they are not the same thing. Machine learning is a subset of artificial intelligence (AI) that enables machines to learn without being explicitly programmed while deep learning…

Machine learning and neural networks are two important technologies in the field of artificial intelligence (AI). While they are often used together, they are not the same thing. Here, we will explore the differences between machine learning and neural networks and how they are related. Let us first understand both the terms in detail and…

Artificial Intelligence and Machine Learning are two buzzwords that are commonly used in the world of technology. Although they are often used interchangeably, they are not the same thing. Artificial intelligence (AI) and machine learning (ML) are related concepts, but they have different definitions, applications, and implications. In this article, we will explore the differences between machine learning…

Machine learning is an interdisciplinary field that involves computer science, statistics, and mathematics. In particular, mathematics plays a critical role in developing and understanding machine learning algorithms. In this chapter, we will discuss the mathematical concepts that are essential for machine learning, including linear algebra, calculus, probability, and statistics. Linear Algebra Linear algebra is the branch…

Data structure plays a critical role in machine learning as it facilitates the organization, manipulation, and analysis of data. Data is the foundation of machine learning models, and the data structure used can significantly impact the model’s performance and accuracy. Data structures help to build and understand various complex problems in Machine learning. A careful…

Machine learning has transformed various industries by automating processes, predicting outcomes, and discovering patterns in large data sets. Some real-life examples of machine learning include virtual assistants & chatbots such as Google Assistant, Siri & Alexa, recommendation systems, Tesla autopilot, IBM’s Watson for Oncology, etc. Most of us think that machine learning is something that is related…

Machine learning is a powerful technology that has transformed the way we approach data analysis, but like any technology, it has its limitations. Here are some of the key limitations of machine learning − Dependence on Data Quality Machine learning models are only as good as the data used to train them. If the data…

Machine learning is a rapidly growing field with many promising applications. However, there are also several challenges and issues that must be addressed to fully realize the potential of machine learning. Some of the major challenges and common issues faced in machine learning include − Overfitting Overfitting occurs when a model is trained on a…