Machine Learning

K-Nearest Neighbors (KNN) Classifier and Imputation using KNN

Artificial Intelligence, Machine Learning /

K-Nearest Neighbors (KNN) Classifier and Imputation using KNN Author: Bindeshwar Singh Kushwaha What is K-Nearest Neighbors (KNN)? KNN is a supervised machine learning algorithm. It is easy to understand and does not involve complex math. Commonly used for classification tasks, especially with labeled data. We’ll use the Iris dataset, which has flower measurements. Iris Dataset […]

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Handling Missing Data and Categorical Features

Artificial Intelligence, Machine Learning /

Handling Missing Data and Categorical Features By: Bindeshwar Singh Kushwaha Data Preprocessing Flow Raw Data → Handle Missing Values → Encode Categorical Variables → Feature Scaling → Preprocessed Data Overview of Data Preprocessing Load Titanic dataset from CSV file Handle missing values using various techniques Encode categorical data for machine learning Save the cleaned dataset

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What is Data Preprocessing

Artificial Intelligence, Machine Learning /

What is Data Preprocessing? Why It Matters in Machine Learning! Author: Bindeshwar Singh Kushwaha Real-World Data Challenges Missing or incomplete values Inconsistent formatting and typos Mixed data types (text, numeric, dates) Categorical variables needing encoding Scale variations and outliers What is Data Preprocessing? A set of techniques to clean and prepare raw data Essential for

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Binomial Distribution Mean and Variance Related Problems| Data Sc. and A.I. Lect. Series

Artificial Intelligence, Machine Learning, Probability and Statistics /

Binomial Distribution: Mean and Variance Problem 1: Mean = 3, Variance = 4? Given: Is it possible a binomial distribution has a mean of 3 and a variance of 4? Solution: Mean: \( \mu = np \) Variance: \( \sigma^2 = npq \), where \( q = 1 – p \) Given: \( np =

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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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Moments of Binomial Distribution Video I Data Science and A.I. Lect. Series

Artificial Intelligence, Machine Learning, Probability and Statistics, Research and Development, Statistics /

  Moments of Binomial Distribution By Bindeshwar Singh Kushwaha — PostNetwork Academy Moment Definition Let \( X \sim B(n, p) \) be a binomial random variable. The \( r^\text{th} \) raw moment about origin: \( \mu_r’ = \mathbb{E}(X^r) = \sum_{x=0}^{n} x^r \cdot \mathbb{P}(X = x) \) First-order moment (mean): \( \mu_1′ = \mathbb{E}(X) \) Binomial

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Matrix Operations with PyTorch | Learn Linear Algebra with Code

Artificial Intelligence, Generative A. I., Linear Algebra, Machine Learning, Mathematics, Python, PyTorch /

Matrix Operations with PyTorch Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Matrix Addition and Scalar Multiplication Matrix Addition: We add corresponding elements of the same-sized matrices: \( A + B = [a_{ij} + b_{ij}] \) Scalar Multiplication: Multiply each element of the matrix by the scalar value: \( kA = [k \cdot a_{ij}] \) Example:

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Tensors in Pytorch

Understanding PyTorch: Tensors, Vectors, and Matrices

PyTorch /

Understanding PyTorch: Tensors, Vectors, and Matrices By: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy What is PyTorch? PyTorch is an open-source deep learning framework. It supports dynamic computation graphs. Designed to be Pythonic and flexible. Commonly used for research and production in AI/ML. Types of Tensors A scalar is a 0-dimensional tensor. A vector is a

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