Artificial Intelligence

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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Linear Regression using Gradient Descent

Artificial Intelligence, Machine Learning /

  Linear Regression using Gradient Descent By Bindeshwar Singh Kushwaha General Linear Regression Model We have a collection of labeled examples: $$ \{(\mathbf{x}_i, y_i)\}_{i=1}^{N} $$ \( \mathbf{x}_i \) is a \( D \)-dimensional feature vector \( y_i \) is a real-valued target Each feature \( x_i^{(j)} \in \mathbb{R} \), where \( j = 1, …,

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Label Encoding and One Hot Encoding in Machine Learning

Artificial Intelligence, Machine Learning /

📘 Label Encoding and One-Hot Encoding Author: Bindeshwar Singh Kushwaha 🎯 Encoding Categorical Features 🔹 Label Encoding Assigns each category an integer value Suitable for ordinal data (e.g., size: small, medium, large) Tool: LabelEncoder from sklearn.preprocessing Example (Titanic): Encoding Sex as 0 (male), 1 (female) 🔹 One-Hot Encoding Converts categories into binary columns (one per

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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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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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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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Fine-Tuning Large Language Models (LLMs) ( DistilGPT-2)

Artificial Intelligence, Generative A. I., Research and Development /

Transfer Learning and Fine-Tuning Large Language Models In this post, we will explore the concept of Transfer Learning, its connection to fine-tuning large language models (LLMs), and step-by-step instructions to fine-tune DistilGPT-2. What is Transfer Learning? Transfer Learning is a powerful machine learning technique where a model trained on one task is reused as the

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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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