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 […]
Binomial Distribution Data Science and A.I. Lecture Series Read More »
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
Bernoulli Distribution in Probability and Statistics Read More »
Addition, Multiplication Theorem of Expectation and Covariance Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha PostNetwork Academy Outline Introduction Addition Theorem of Expectation Proof of Addition Theorem Multiplication Theorem of Expectation Proof of Multiplication Theorem Covariance Introduction Expectation (or expected value) is a fundamental concept in probability and statistics. It provides a measure
Addition, Multiplication Theorem of Expectation and Covariance Read More »
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
Moments and Other Measures in Terms of Expectations Read More »
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
Mathematical Expectation Read More »
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
Operators in Python Programming Read More »
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
Understanding Input and Output in Python Read More »
Variables and Assignment in Python What is a Variable? A variable is a name that refers to a value stored in memory. It acts as a reference, not a storage container. Unlike other languages, Python variables do not store values but rather point to objects in memory. Example: x = 10 # x refers
Variables and Assignment in Python Read More »
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
Bivariate Continuous Random Variables Read More »
IBM Research and Hugging Face Introduce SmolDocling: A Compact Vision-Language Model for Document Conversion IBM Research and Hugging Face have unveiled SmolDocling, an ultra-compact vision-language model designed for end-to-end document conversion. Unlike traditional models that rely on large foundational architectures or complex pipelines, SmolDocling offers a lightweight, efficient solution for processing entire documents while preserving