Understanding Stemming and Lemmatization in NLP

Stemming and Lemmatization are text normalization techniques in Natural Language Processing (NLP) that reduce words to their base forms, but they differ in their approach: stemming is a rule-based, fast, and potentially inaccurate method, while lemmatization is context-aware, dictionary-based, and more accurate but slower. Stemming Definition: A heuristic process that removes suffixes or prefixes from […]

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What are the different dimensionality reduction techniques?

Dimensional reduction is a technique used in data processing to reduce the number of input variables in a dataset. This process simplifies the data while retaining its essential characteristics, which can help in several ways, such as improving model performance, reducing computational cost, and facilitating data visualization. Here are some common methods and concepts related

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Candlestick Mastery – Introduction

Candlestick mastery is understanding and using candlestick patterns to make informed trading decisions. Candlestick patterns are graphical representations of price movement, and they can be used to identify trends, reversals, and support and resistance levels. There are many different candlestick patterns, but some of the most common and important ones include: Bullish patterns: Bullish patterns suggest

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Essentials of Data Science – Probability and Statistical Inference – Normal Approximation

In this note series on Probability and Statistical Inference, we have already seen the importance of probability distributions and their associated probability functions for discrete random variables and continuous random variables. In addition, we have learned to resemble a natural random phenomenon with these probability distributions. These distributions were Degenerate distribution, Uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, Geometric distribution, and Normal

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Python Fundamentals – String

A string is a sequence of characters. Generally, computers do not deal with characters, they deal with numbers. Even though you may see characters on your screen, internally it is stored and manipulated as a combination of 0’s and 1’s. The conversion of a character to a number is called encoding, and the reverse process

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Essentials of Data Science – Probability and Statistical Inference – Normal Distribution

In this note series on Probability and Statistical Inference, we have already seen the importance of probability distributions and their associated probability functions for discrete random variables. In addition, we have learned to resemble a natural random phenomenon with these probability distributions. These distributions were Degenerate distribution, Uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, and Geometric distribution. This note will cover probability distributions and

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Essentials of Data Science – Probability and Statistical Inference – Continuous Uniform Distribution

In this note series on Probability and Statistical Inference, we have already seen the importance of probability distributions and their associated probability functions for discrete random variables. These distributions were Degenerate distribution, Uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, and Geometric distribution. In addition, we have learned to resemble a natural random phenomenon with these probability distributions. This note will cover probability distributions and

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Essentials of Data Science – Probability and Statistical Inference – Geometric Distribution

In this note series on Probability and Statistical Inference, we have seen the importance of probability distributions such as Bernoulli distribution, Binomial distribution, and Poisson distribution, and how these distributions resemble a real random phenomenon. This note will cover the basic intuition behind Geometric distribution, expectation, variance, and other quantitative measures to characterize the geometric random

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