Machine Learning

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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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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Mathematical Expectation

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

Mathematical Expectation Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha – PostNetwork Academy Introduction This unit explores the expectation of a random variable. Expectation provides a measure of central tendency in probability distributions. Expectation is useful in both discrete and continuous probability distributions. Problems and examples help in understanding practical applications. Objectives Define

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Operators in Python Programming

Artificial Intelligence, Machine Learning, Programming, Python /

  Operators in Python Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha | Institute: PostNetwork Academy Introduction Operators in Python are special symbols that perform computations on operands. Python provides various types of operators: Arithmetic Operators Relational Operators Logical Operators Bitwise Operators Assignment Operators Membership Operators Identity Operators Arithmetic Operators a = 10

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Understanding Input and Output in Python

Machine Learning, Programming, Python /

  Python Input and Output Understanding Input and Output in Python Author: Bindeshwar Singh Kushwaha | Institute: PostNetwork Academy What is Input and Output? In Python, input and output refer to the mechanisms by which a program interacts with users. Input: Data provided by the user using the input() function. Output: Information displayed using the

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Bivariate Continuous Random Variables

Machine Learning, Mathematics, Probability and Statistics /

  Bivariate Continuous Random Variables Introduction A bivariate continuous random variable extends the concept of a single continuous random variable to two dimensions. It describes situations where two variables vary continuously and have some form of dependence or interaction. Understanding these concepts is fundamental in probability theory, statistics, and data science. Objectives Define bivariate continuous

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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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Continuous Cumulative Distribution Function (CDF) | Probability & Statistics

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

  Definition: Continuous CDF A continuous random variable can take an infinite number of values in a given range. The Probability Density Function (PDF) \( f(x) \) describes the likelihood of \( X \) falling within a small interval. The Cumulative Distribution Function (CDF) is given by: \[ F(x) = P[X \leq x] = \int_{-\infty}^{x}

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Ordinary Least Squares (OLS) Regression: Step-by-Step Guide with Derivation & Visualization

Artificial Intelligence, Machine Learning /

Ordinary Least Squares (OLS) Regression Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Dataset of a Company X (Budget) Y (Sales) 1 2 2 2.8 3 3.6 4 4.5 5 5.1 Description: The dataset represents the relationship between advertising budget (\(X\)) and sales revenue (\(Y\)). The company wants to analyze how the budget affects sales using

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