Streamlit: The Lightning Builder for Data Applications! Hello everyone, I am Yan Yu. Today we are going to explore Streamlit, a powerful tool that allows you to quickly create data science and machine learning applications. Whether you are a beginner in Python or an experienced developer, Streamlit can help you easily turn your data analysis … Read more
Streamlit: The Amazing Python Library for Data Applications! Hello everyone! Today I want to share an amazing Python library – Streamlit. Are you still struggling with how to present your data analysis results? Are you still worried about creating interactive visual interfaces? With Streamlit, you can easily create beautiful web applications with just a few … Read more
Streamlit: A Python Tool for Rapid Data App Development Hello everyone, I am your friend. Today we are going to explore Streamlit, a powerful Python library designed for quickly building and deploying data science applications. Whether you are a data scientist, engineer, or programming enthusiast, Streamlit provides a wealth of tools to showcase and analyze … Read more
1. Overview of Machine Learning 1) What is Machine Learning Artificial Intelligence (Artificial intelligence) is a new scientific discipline that studies and develops theories, methods, technologies, and application systems to simulate, extend, and enhance human intelligence. It is a broad and general concept, and the ultimate goal of artificial intelligence is to enable computers to … Read more
Artificial Intelligence (AI) is rapidly becoming a core technology across multiple industries, including the field of data science. Individuals and companies that actively adopt AI technologies may become industry leaders, while those resisting technological change may be surpassed by competitors using AI. AI is no longer a fleeting trend but is gradually becoming an integral … Read more
Python Algorithm Implementation from scipy import stats from pylab import * def knnClassify(Target_feature, dataSet, p, k): dataSet_x = dataSet[:, 0:-1] dataSet_y = dataSet[:, -1] L_p = [] for i in range(len(dataSet_x)): l_p = 0 for x_i in range(len(dataSet_x[i])): l_p = (abs(dataSet_x[i][x_i] – Target_feature[x_i]))**p+l_p l_p =math.pow(l_p, 1/p) L_p.append(l_p) L_p = np.array(L_p) y_i = L_p.argsort() y = … Read more
Author: AISHWARYA SINGH, AUGUST 22, 2018 Translator: ZHAO XUEYAO Proofreader: ZHANG LING This article is approximately 4200 words, and is recommended for a 10 minute read. This article explains the basic working principle of the K-Nearest Neighbors (KNN) algorithm and briefly introduces three methods for calculating the distance between points. Introduction Among all the machine … Read more
K-Nearest Neighbor (KNN) classification algorithm is a theoretically mature method and one of the simplest machine learning algorithms. The idea behind this method is: in the feature space, if the majority of the k nearest samples (i.e., the closest samples in the feature space) around a sample belong to a certain category, then that sample … Read more
The k-means algorithm is one of the most commonly used methods for unsupervised clustering due to its simplicity and good applicability to large sample data. This article provides a detailed summary of the principles of the k-means clustering algorithm. Table of Contents 1. Principles of k-means Clustering Algorithm 2. Steps of k-means Clustering Algorithm 3. … Read more
Author: Wang Jiaxin Reviewed by: Chen Zhiyan This article is about 5800 words and is recommended to read for more than 10 minutes. This article introduces the classic K-Means clustering algorithm. Overview As is well known, machine learning algorithms can be divided into supervised learning and unsupervised learning. Supervised learning is often used for classification … Read more