# Blog

## What is Regularization in Machine Learning?

Regularization helps to solve the problem of overfitting in machine learning. How well a model fits training data determines how well it performs on unseen data. Poor performance can occur due to either overfitting or underfitting the data. Overfitting is a phenomenon...

## How to Calculate Jaccard Similarity in Python

Understanding the similarity between two objects is a universal problem. In machine learning, you can use similarity measures for various issues. These include object detection, classification and segmentation tasks in computer vision and similarity between text...

## Top 12 Python Libraries for Data Science and Machine Learning

Machine learning is the science of programming a computer to learn from different data and perform inference. Yesteryear, machine learning tasks involved manual coding all of the algorithms and mathematical and statistical formulae. Nowadays, we have fantastic...

## The History of Reinforcement Learning

Reinforcement learning (RL) is an exciting and rapidly developing area of machine learning that significantly impacts the future of technology and our everyday lives. RL is a field separate from supervised and unsupervised learning focusing on solving problems through...

## Introduction to Pandas: A Complete Tutorial for Beginners

Pandas is an open-source library providing high-performance, easy-to-use data structures, and data analysis tools for Python. It is one of the fundamental tools for data scientists and can be thought of like Python's Excel. With Pandas, you can work with many...

## The Best Books For Machine Learning for Both Beginners and Experts

Machine learning (ML) is an exciting and rapidly expanding domain in Computer Science. ML is a field of study devoted to the automated improvement of computer algorithms through exposure to data. The knowledge base underneath ML consists of a broad range in topics in...

## The History of Machine Learning

Machine learning is an exciting and rapidly developing field of study centered around the automated improvement (learning) of computer algorithms through experience with data. Through persistent innovation and research, the capabilities of machine learning are now in...

## 5 Significant Benefits of Online Learning for Data Science

The internet has made access to information very easy and affordable. Technology has been completely integrated into how we learn and how we work. It can all be supplemented or entirely provided by online education from primary school to degree level. Learning within...

## 7 Best Tips to Help Get a Data Scientist Job From Scratch

You have developed a passion and now you want to embark on a new career, but you are unsure where to start to enter the space of data science. This post will provide you with clear, practical steps to get on the road to a rewarding and stimulating career path. The...

## Paper Reading #2: XLNet Explained

One of the most celebrated, recent advancements in language understanding is the XLNet model from Carnegie Mellon University and Google. It takes the "best-of-both-worlds" approach by combining auto-encoding and autoregressive language modeling to achieve...

## 9 Best Tips For Early Career Research Focused Data Scientists

Embarking on a career in data science is an exciting challenge that requires a lot of initiative and a desire to learn and apply knowledge quickly. For research scientists, there is an emphasis on experimentation and scientific discovery. The methods and objectives...

## How to Solve R Warning: aggregate function missing, defaulting to length

This warning occurs when you use the dcast function to convert a data frame from long to wide format, but more than one value can be placed in the individual output cells of the wide data frame. You can stop this warning from occurring by specifying the aggregate...

## How to Calculate Implied Volatility in C++

Implied volatility tells us the expected volatility of a stock over an optionâ€™s lifetime. Implied volatility is directly influenced by the supply and demand of the options and the marketâ€™s expectation of the direction of the price of the underlying security. As...

## How to Calculate Implied Volatility in R

Implied volatility tells us the expected volatility of a stock over an option's lifetime. Implied volatility is directly influenced by the supply and demand of the options and the market's expectation of the direction of the price of the underlying security. As...

## How to Solve R Error in solve.default() Lapack routine dgesv: system is exactly singular

This error occurs when you try to use the solve() function, but the matrix you handle is a singular matrix. Singular matrices do not have an inverse. The only way to solve this error is to create a matrix that is not singular. This tutorial will go through the error...

## How to Solve R Warning: `summarise()` has grouped output by â€˜Xâ€™. You can override using the `.groups` argument

This R warning occurs when you have more than one column in group_by when using the dplyr::summarise(). The summarise function has a .groups argument with a default value of 'drop_last'. If you set the .groups argument manually, the warning will not appear. It is only...

## How to Solve R Error in scan: Line 1 did not have X elements

This error occurs when you try to import a dataset into R, and there is data missing in the file. You can solve this error by checking for special characters, ensuring that you have the correct number of headings, or by using the fill argument when reading the file....

## How to Calculate Implied Volatility in Python

Implied volatility tells us the expected volatility of a stock over an option's lifetime. Implied volatility is directly influenced by the supply and demand of the options and the market's expectation of the direction of the price of the underlying security. As...

## How to Derive Options Greeks from the Black-Scholes Formula

Options Greeks are a set of quantities representing an option's price sensitivity to its underlying parameters. Each of them measures a different dimension of the risk in an option position. They fall out elegantly from derivatives of the Black-Scholes options pricing...

## Black-Scholes Option Pricing in C++

The Black-Scholes or Black-Scholes-Merton model is a financial mathematical equation for pricing options contracts and other derivatives. Fischer Black and Myron Scholes published the formula in their 1973 paper "The Pricing of Options and Corporate Liabilities"....

## Black-Scholes Option Pricing in Python

The Black-Scholes or Black-Scholes-Merton model is a financial mathematical equation for pricing options contracts and other derivatives. Fischer Black and Myron Scholes published the formula in their 1973 paper "The Pricing of Options and Corporate Liabilities"....