k-means clustering is an unsupervised learning algorithm used to partition a given dataset into several groups (clusters). It works by iteratively updating the “centroids” – the center points of each cluster – and assigning data points to the closest centroid. Features: Steps of k-Means Clustering Let’s consider the following 2D dataset as an example: We want to cluster… Read More »
Basic Concept Example: Classifying Flower Types with KNN Data: Flower Color Petal Length Type Red 5.0 Morning Glory Red 4.8 Morning Glory White 6.2 Lily White 6.0 Lily Yellow 7.1 Rose Yellow 6.9 Rose Unknown Data: Predict the type of a flower with Red color and Petal Length of 5.2. Steps: Python Code Example (using scikit-learn) Advantages and Disadvantages… Read More »
Cosine similarity is a metric used to measure the similarity between two vectors. It’s commonly employed for comparing high-dimensional data, such as text data. Features: Example: Let’s say we have two users, A and B, who rate movies using vectors. In this case, users A and B have similar ratings for Action and Comedy, so the cosine similarity… Read More »
The Monte Carlo method is a technique that uses random numbers to solve complex problems. For example, consider the following types of problems: These problems can be difficult to solve analytically. The Monte Carlo method involves performing experiments using a large number of randomly generated numbers and making statistical estimations based on the results. Origin of the name: It’s named… Read More »
Hash table search is an algorithm for efficiently searching data. Instead of looking sequentially like in a regular array, it uses a “hash function” to convert the data into an “index” where the data is stored, enabling fast access. Imagine: Let’s consider finding a book in a library. How Hash Table Search Works Code Explanation Advantages & Disadvantages… Read More »
Binary search is a very efficient method for finding a specific value within a sorted list of data. Think of it like looking up a word in a dictionary. Specific Steps: Advantages: Disadvantages: In Summary: Binary search is a powerful algorithm for efficiently finding a specific value within sorted data. Understanding the dictionary analogy can help you grasp how… Read More »
Period (T) Frequency (f) Relationship Period and Frequency are inversely proportional to each other. They are related by the following formulas: f=1T T=1f Feature Period (T) Frequency (f) Meaning Time for one cycle Cycles per second Unit Seconds (s) Hertz (Hz) Relationship T = 1/f f = 1/T Analogy: “By the way, ocean waves travel through water, and… Read More »
Calculate the Σ symbol expression that represents the sum of natural numbers using NumPy. What is NumPy? NumPy is a Python library for numerical computation. It’s particularly strong in array operations (vectors and matrices) and allows for fast calculations. We can efficiently perform calculations like summing natural numbers. Code Explanation Why isn’t the end value included? There are… Read More »
The Least Squares Method is a way to find the best-fitting line for your data. It might sound complicated, but it’s similar to thinking you do in everyday life. Example: Ice Cream Sales and Temperature You run an ice cream shop. Every day, you record the highest temperature of the day and how many ice creams you sell. Highest… Read More »
SymPy allows you to easily solve linear equations as follows: 2x+3=7 Defining the Equation and Declaring Variables Describe the equation you want to solve in SymPy and declare the variables you will be using. This code does the following: This code does the following: This result indicates that the solution to the equation 2x + 3 = 7 is x =… Read More »