📘 Poisson Distribution Numerical Examples Author: Bindeshwar Singh Kushwaha Institution: PostNetwork Academy Example 1: Truck Arrivals The number of heavy trucks arriving at a railway station follows a Poisson distribution with an average of 2 arrivals per hour. Find: (a) Probability that no truck arrives (b) Probability that at least two trucks arrive Let \(
Poisson Distribution Numerical Examples Read More »
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
Feature Scaling in Machine Learning : Preprocessing Technique Read More »
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, …,
Linear Regression using Gradient Descent Read More »
📘 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
Label Encoding and One Hot Encoding in Machine Learning Read More »
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
K-Nearest Neighbors (KNN) Classifier and Imputation using KNN Read More »
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
Handling Missing Data and Categorical Features Read More »
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
What is Data Preprocessing Read More »
📘 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
Poisson Distribution | Data Sc. and A.I. Lect. Series Read More »
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 =
Binomial Distribution Mean and Variance Related Problems| Data Sc. and A.I. Lect. Series Read More »
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
Fine-Tuning Large Language Models (LLMs) ( DistilGPT-2) Read More »