TAFL
Notes-1-TAFL, Automaton, Alphabet, String, Language, DFA Notes-2-TAFL-With-Question and Answers
Python for A.I. (ML DL GENAI LLMS AGENTIC AI ) and ROBOTICS
No. Topic Complete (Post + Video + PDF + Quiz) 1 Python Programming Basics 1.1 Introduction to Python & Installation 🔗 1.2 Variables, Data Types & Operators 🔗 1.3 Conditional Statements 🔗 1.4 Loops (for, while) 🔗 1.5 Functions & Recursion 🔗 1.6 Lists, Tuples, Sets, Dictionaries 🔗 1.7 File Handling & Exception Handling 🔗
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Normal Distribution A Detailed Step-by-Step Explanation
Normal Distribution A Detailed Step-by-Step Explanation By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Random Variables A random variable (r.v.) is a function that assigns a numerical value to each outcome of a random experiment. There are two main types of random variables: Discrete Random Variable: Takes countable values (e.g., number of heads in 3 coin
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Negative Binomial Distribution | Simple Explanation #177 Data Sc. and A.I. Lect. Series
Negative Binomial Distribution A Detailed Step-by-Step Explanation By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Relation with Geometric Distribution The negative binomial distribution is a generalization of the geometric distribution. It describes the number of failures before the \( r^{th} \) success in a sequence of Bernoulli trials. When \( r = 1 \), it reduces
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Support Vector Machines Made Easy | SVM Explained with Example
Support Vector Machine (SVM) A Simple Numerical Example – Detailed Explanation Author: Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Type and Purpose of SVM Type of Algorithm: Supervised Machine Learning Algorithm Used for Classification and Regression (SVR) Discriminative Model – finds decision boundaries Known as a Maximum-Margin Classifier Purpose: Find the optimal hyperplane that separates classes
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Geometric Distribution Made Simple | Stepwise Approach #176 Data Sc. and A.I. Lect. Series
Geometric Distribution Made Simple | Stepwise Approach Bindeshwar Singh Kushwaha PostNetwork Academy Geometric Distribution Let a sequence of Bernoulli trials be performed, each with constant probability \(p\) of success and \(q = 1 – p\) of failure. Trials are independent, and we continue performing them until the first success occurs. Let \(X\) be the
Hypergeometric Distribution A Distribution of Dependent Events #175 Data Sc. and A.I. Lect. Series
Hypergeometric Distribution : A Distribution of Dependent Events By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction In the previous sections, we studied distributions such as the binomial distribution. The binomial distribution assumes that each trial is independent and the probability of success remains constant. However, in many real-life problems, selections are made without replacement.
Discrete Uniform Distribution in Statistics
Discrete Uniform Distribution By: Bindeshwar Singh Kushwaha PostNetwork Academy Discrete Uniform Distribution A random variable \( X \) is said to have a discrete uniform distribution if it takes integer values from \( a \) to \( b \) with equal probability. The number of possible values is \[ n = b – a +
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Naive Bayes Classification Algorithm for Weather Dataset
Naive Bayes Classification Algorithm for Weather Dataset Author: Bindeshwar Singh Kushwaha | PostNetwork Academy Introduction to Naive Bayes Classifier Naive Bayes is a probabilistic classification algorithm. It is based on Bayes’ Theorem and the naive independence assumption. Suppose we have a feature vector \(\mathbf{X} = (x_1, x_2, …, x_n)\) and a class \(y\). Bayes Theorem:
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Text Classification with Bag of Words and Naive Bayes
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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