Data Handling

Text Classification with Bag of Words and Naive Bayes

Data Handling, Machine Learning, Statistics /

Text Classification with Bag of Words and Naive Bayes Author: Bindeshwar Singh Kushwaha | PostNetwork Academy Understanding Text with Machine Learning Processing and understanding text allows extraction of meaningful information from raw data. Text data can be structured into features that machine learning algorithms can analyze. Machine learning approaches include supervised, unsupervised, and deep learning […]

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Feature Scaling in Machine Learning : Preprocessing Technique

Artificial Intelligence, Data Handling, Machine Learning, Research and Development /

Feature Scaling in Machine Learning Author: Bindeshwar Singh Kushwaha Postnetwork Academy Why Feature Scaling? Machine learning algorithms often struggle when input features have different scales. Example: Total number of rooms might range from 6 to 39,320, while median incomes range from 0 to 15. Two common methods to scale features: Min-Max Scaling (Normalization) Standardization Min-Max

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Poisson Distribution | Data Sc. and A.I. Lect. Series

Artificial Intelligence, Data Handling, Machine Learning, Mathematics, Probability and Statistics /

📘 Understand Poisson Distribution 📌 Introduction In binomial distributions, events’ occurrences and non-occurrences are equally important. However, in real-life situations: Events do not occur as outcomes of fixed number of trials. Events occur randomly over time. Interest lies only in the number of occurrences. Examples: Number of printing mistakes per page in a book. Number

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Fitting Binomial Distribution | Data Science and A.I. Lecture Series

Artificial Intelligence, Data Handling, Probability and Statistics, Statistics /

Fitting Binomial Distribution Introduction Fitting a binomial distribution involves comparing observed frequencies with expected frequencies derived from the binomial probability formula. The recurrence relation simplifies the process of finding probabilities. This technique is useful for testing if a dataset follows a binomial distribution. Binomial Probability Function The binomial probability function is: $$p(x) = {n \choose

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Building a Smart Indian Food Recommender Using TinyLlama, Ollama, and Python

Artificial Intelligence, Data Handling, Deep Learning, Generative A. I., IoT, Machine Learning /

Building a Smart Indian Food Recommender Using TinyLlama, Ollama, and Python In this tutorial, we’ll build a simple yet smart food recommender system that suggests Indian dishes based on the current humidity and temperature values. The system uses a local language model TinyLlama running via Ollama, and Python’s random.randint() function to simulate real-time weather. Why

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Binomial Distribution Data Science and A.I. Lecture Series

Artificial Intelligence, Data Handling, Generative A. I., Mathematics, Maths, Probability and Statistics, Research and Development, Statistics /

  Binomial Distribution Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha | PostNetwork Academy Binomial Probability Function The binomial probability function is given by: \[ P(X = k) = \binom{n}{k} p^k (1 – p)^{n – k} \] where: \( n \) = total number of trials \( k \) = number of successes

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Bernoulli Distribution in Probability and Statistics

Artificial Intelligence, Data Handling, Machine Learning, Mathematics, Maths, Research and Development, Statistics /

Bernoulli Distribution Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha | PostNetwork Academy Introduction to Bernoulli Distribution A Bernoulli trial is an experiment with only two possible outcomes: Success (1) and Failure (0). If p is the probability of success, then q = 1 – p is the probability of failure. A random

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Moments and Other Measures in Terms of Expectations

Artificial Intelligence, Data Handling, Machine Learning, Mathematics, Maths, Research and Development, Statistics /

  Moments and Other Measures in Terms of Expectations Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha – PostNetwork Academy Moments The \( r^{th} \) order moment about any point \( A \) of a variable \( X \) is given by: For discrete variables: \[ \mu_r’ = \sum_{i=1}^{n} p_i (x_i – A)^r

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Bivariate Discrete Cumulative Distribution Function

Artificial Intelligence, Data Handling, Machine Learning, Maths, Probability and Statistics, Research and Development, Statistics /

Bivariate Discrete Cumulative Distribution Function Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Joint and Marginal Distribution Functions for Discrete Random Variables Two-Dimensional Joint Distribution Function The distribution function of the two-dimensional random variable \((X, Y)\) for all real \(x\) and \(y\) is defined as: \[ F(x,y) = P(X \leq

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Introduction to Machine Learning

Artificial Intelligence, Data Handling, Machine Learning /

Introduction to Machine Learning Definition and Types Welcome to this detailed introduction to Machine Learning. This post explores the fundamental definitions, types of machine learning, and their mathematical representations. What is Machine Learning? What is Machine Learning? What are the different types of Machine Learning? How can we mathematically define each type? Definition of Machine

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