Naveen, Author at Nomidl

Overfitting in Machine Learning: What it is and When it Occurs

In machine learning, overfitting refers to the phenomenon where a model performs well with training data, but does not generalize well to new, unseen data. Overfitting occurs when the model is too complex for the amount of training data. To understand overfitting, let’s look at an analogy. Imagine you are in a foreign country and…

Understanding Different Types of Machine Learning: Batch, Online, Instance-Based, and Model-Based Learning

Machine learning is an integral part of artificial intelligence (AI). It allows computer systems to learn from data and improve their performance. There are different types of machine learning, such as batch learning, online learning, example-based learning, and model-based learning. In this article, we will explore each of these types in detail and understand their…

Important Supervised and Unsupervised Algorithms for Machine Learning

Machine learning is a branch of computer science and artificial intelligence that allows machines to learn automatically without special programming. It involves using algorithms and statistical models to analyze and interpret data and make predictions based on that analysis. Machine learning can be broadly divided into two types of algorithms: supervised and unsupervised. In this…

What are the Challenges of Natural Language Processing

Natural Language Processing is that the field of design methods and algorithms that takes as input or produce as output unstructured. Human language is highly ambiguous (consider the sentence I ate pizza with friends, and compare it to I ate pizza with olives), and also highly variable (the core message of I ate pizza with…

Stemming vs Lemmatization Difference: Explained in Detail

Introduction When dealing with large amount of text data, it becomes essential to preprocess and analyze the text effectively. Stemming and lemmatization are text processing techniques that help reduce words to their base forms, helps you in better analysis and understanding. Stemming Stemming is a technique that aims to reduce words to their root form,…

Reinforcement Learning: Maximizing Rewards through Continuous Learning and Markov Decision Processes

Reinforcement learning (RL) is a subfield of machine learning that focuses on using reward functions to train agents to make decisions and actions in an environment that maximizes their cumulative reward over time. RL is one of the three main machine learning paradigms, along with supervised and unsupervised learning. There are two main types of…

What is unsupervised learning and how is it used?

Unsupervised learning is a type of machine learning in which an algorithm examines data without labeled training samples or feedback. The goal is to find hidden patterns and relationships in the data. This is in contrast to supervised learning, where an algorithm learns from labeled inputs and outputs. Unsupervised learning algorithms are also called clustering…

What is Supervised Learning?

Supervised learning is a type of machine learning where a computer is taught using examples of real data and “known” data, where the teacher knows the correct answer and teaches someone else. Learning can take any form, from simple human feedback or input to a more complex model that predicts the outcome of future events.…

Tokenization in NLP: Breaking Language into Meaningful Words

Tokenization is a fundamental concept in Natural Language Processing (NLP) that involves breaking down text into smaller tokens. Whether you’ve heard of tokenization before or not, this article will help you get the clear and concise explanation. What is Tokenization? Tokenization is the process of dividing a given text, such as a document, paragraph, or…

What is the difference between Computer Vision and Photo Enhancement?

Computer vision is a field of computer science that studies how computers can be made for the purpose of interpreting images, videos, and other forms of data. It is often called machine vision or computer vision. And it deals with processing and understanding image data. This includes methods for acquiring, storing, analyzing, recognizing, and interpreting…