ODSC - Open Data Science

JANUARY 5, 2024

Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools. Pandas provides a fast and efficient way to work with tabular data. It is widely used in data science, finance, and other fields where data analysis is essential.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Chatbots Life

APRIL 16, 2024

By applying generative models in these areas, researchers and practitioners can unlock new possibilities in various domains, including computer vision, natural language processing, and data analysis. Support Vector Machines (SVM): SVM finds an optimal hyperplane to separate different classes in high-dimensional spaces.

Natural Language Processing 59

Natural Language Processing Support Vector Machines Machine Learning Machine Learning 59

Dataconomy

AUGUST 9, 2023

How could machine learning be used in network traffic analysis? Machine learning is fundamentally changing the landscape of network traffic analysis by automating the process of data analysis and interpretation.

Machine Learning 91

Machine Learning Machine Learning ML ML 91

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

IBM Journey to AI blog

JULY 6, 2023

Machine learning can then “learn” from the data to create insights that improve performance or inform predictions. Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

IBM Journey to AI blog

DECEMBER 19, 2023

The main difference being that while KNN makes assumptions based on data points that are closest together, LOF uses the points that are furthest apart to draw its conclusions. Unsupervised learning Unsupervised learning techniques do not require labeled data and can handle more complex data sets.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Heartbeat

SEPTEMBER 28, 2023

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Support Vector Machine (svm): Versatile model for linear and non-linear data.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Heartbeat

MARCH 6, 2024

I will start by looking at the data distribution, followed by the relationship between the target variable and independent variables. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Exploratory Data Analysis 52

Exploratory Data Analysis Support Vector Machines Data Analysis Data Analysis 52

IBM Journey to AI blog

AUGUST 28, 2023

Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Machine Learning 68

Machine Learning Machine Learning Data Mining Data Mining 68

Heartbeat

OCTOBER 25, 2023

The field demands a unique combination of computational skills and biological knowledge, making it a perfect match for individuals with a data science and machine learning background. Deep learning, a subset of machine learning, has revolutionized image analysis in bioinformatics.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

AWS Machine Learning Blog

FEBRUARY 25, 2025

Its internal deployment strengthens our leadership in developing data analysis, homologation, and vehicle engineering solutions. Classification algorithms like support vector machines (SVMs) are especially well-suited to use this implicit geometry of the data.

Algorithm 69

Algorithm Machine Learning Machine Learning K-nearest Neighbors 69

Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

FEBRUARY 24, 2023

It could be anything from customer service to data analysis. Collect data: Gather the necessary data that will be used to train the AI system. This data should be relevant, accurate, and comprehensive. Several algorithms are available, including decision trees, neural networks, and support vector machines.

Artificial Intelligence 91

Artificial Intelligence Artificial Intelligence AI AI 91

Mlearning.ai

MAY 23, 2023

The following Venn diagram depicts the difference between data science and data analytics clearly: 3. Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. Another example can be the algorithm of a support vector machine.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 3, 2023

49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales. Anomalies might have low probabilities under the fitted GMM, as they deviate from the common Gaussian patterns observed in normal data.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Heartbeat

MARCH 8, 2023

Text categorization is supported by a number of programming languages, including R, Python, and Weka, but the main focus of this article will be text classification with R. R Language Source: i2tutorial R, a popular open-source programming language, is used for statistical computation and data analysis.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Mining 52

PyImageSearch

SEPTEMBER 16, 2024

Anomaly detection ( Figure 2 ) is a critical technique in data analysis used to identify data points, events, or observations that deviate significantly from the norm. Supervised Learning These methods require labeled data to train the model. The model learns to distinguish between normal and abnormal data points.

Clustering 56

Clustering Algorithm Machine Learning Machine Learning 56

DagsHub

APRIL 21, 2024

Moving the machine learning models to production is tough, especially the larger deep learning models as it involves a lot of processes starting from data ingestion to deployment and monitoring. It provides different features for building as well as deploying various deep learning-based solutions.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Mlearning.ai

FEBRUARY 15, 2023

Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. Deep ensemble learning models utilise the benefits of both deep learning and ensemble learning to produce a model with improved generalisation performance.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 18, 2024

The algorithm you select depends on the nature of the problem and the type of data you have. spam detection), you might choose algorithms like Logistic Regression , Decision Trees, or Support Vector Machines. For unSupervised Learning tasks (e.g., For instance: For a classification problem (e.g.,

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52