PyImageSearch

SEPTEMBER 16, 2024

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? By leveraging anomaly detection, we can uncover hidden irregularities in transaction data that may indicate fraudulent behavior.

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57

Smart Data Collective

JUNE 4, 2021

it is overwhelming to learn data science concepts and a general-purpose language like python at the same time. Exploratory Data Analysis. Exploratory data analysis is analyzing and understanding data. For exploratory data analysis use graphs and statistical parameters mean, medium, variance.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., Taxonomy of the self-supervised learning Wang et al. 2022’s paper.

Supervised Learning 52

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors 52

Pickl AI

NOVEMBER 4, 2024

This article will guide you through effective strategies to learn Python for Data Science, covering essential resources, libraries, and practical applications to kickstart your journey in this thriving field. Key Takeaways Python’s simplicity makes it ideal for Data Analysis. in 2022, according to the PYPL Index.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Dataconomy

MAY 25, 2023

Here are some ways AI enhances IoT devices: Advanced data analysis AI algorithms can process and analyze vast volumes of IoT-generated data. By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Pickl AI

JULY 17, 2023

The ability of unsupervised learning to discover similarities and differences in data makes it ideal for conducting exploratory data analysis. Unsupervised Learning Algorithms Unsupervised Learning Algorithms tend to perform more complex processing tasks in comparison to supervised learning.

Machine Learning 52

Machine Learning Machine Learning Clustering K-nearest Neighbors 52

Pickl AI

NOVEMBER 13, 2024

Summary: Machine Learning is categorised into four main types: supervised, unsupervised, semi-supervised, and Reinforcement Learning. Each type employs distinct methodologies for Data Analysis and decision-making. UnSupervised Learning uncovers hidden patterns in unlabelled datasets.

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Natural Language Processing 52

Dataconomy

MAY 6, 2025

The K-Nearest Neighbor (KNN) algorithm is an intriguing method in the realm of supervised learning, celebrated for its simplicity and intuitive approach to predicting outcomes. Often employed for both classification and regression tasks, KNN leverages the proximity of data points to derive insights and make decisions.

K-nearest Neighbors 91

K-nearest Neighbors Algorithm Machine Learning Machine Learning 91

Pickl AI

AUGUST 1, 2023

It helps in discovering hidden patterns and organizing text data into meaningful clusters. Topic Modeling and Document Clustering: Build a text mining project that performs topic modeling and document clustering. Cluster similar documents based on their content and explore relationships between topics.

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Pickl AI

OCTOBER 9, 2023

This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming. Unsupervised learning algorithms Unsupervised learning algorithms are a vital part of Machine Learning, used to uncover patterns and insights from unlabeled data.

Machine Learning 98

Machine Learning Machine Learning Algorithm ML 98

Towards AI

APRIL 4, 2024

A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.

K-nearest Neighbors 80

K-nearest Neighbors Machine Learning Machine Learning Decision Trees 80

Pickl AI

JULY 12, 2024

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Becoming Human

MAY 15, 2023

These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey. Data Analysis After learning math now, you are able to talk with your data.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

SEPTEMBER 17, 2024

Summary: Dimensionality reduction is a crucial technique in data analysis that simplifies complex datasets by reducing the number of features while retaining essential information. Dimensionality reduction in Machine Learning enhances model performance and improves data visualisation by focusing on the most significant dimensions.

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. predicting house prices).

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

OCTOBER 2, 2024

Genomic Analysis is crucial for various applications, including personalised medicine, cancer research, genetic disorder diagnosis, and evolutionary biology. AI Techniques Used in Genomic Analysis AI encompasses a range of techniques that can be applied to genomic Data Analysis.

Machine Learning 52

Machine Learning Machine Learning Artificial Intelligence AI 52

Pickl AI

FEBRUARY 12, 2024

This theorem is crucial in inferential statistics as it allows us to make inferences about the population parameters based on sample data. Differentiate between supervised and unsupervised learning algorithms. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. Explain the difference between supervised and unsupervised learning.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 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 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

FEBRUARY 7, 2025

Features : The attributes or characteristics of the data used to make predictions. Types of Machine Learning Machine Learning is divided into three main types based on how the algorithm learns from the data: Supervised Learning In supervised learning , the algorithm is trained on labelled data.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

DECEMBER 4, 2024

Ensuring data quality is vital for producing reliable results. Machine Learning Understanding Machine Learning algorithms is essential for predictive analytics. This includes supervised learning techniques like linear regression and unsupervised learning methods like clustering.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

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. Unsupervised learning techniques, such as clustering and dimensionality reduction, aid in identifying patterns and structures within datasets.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

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

Pickl AI

OCTOBER 20, 2024

You should have a good grasp of linear algebra (for handling vectors and matrices), calculus (for understanding optimisation), and probability and statistics (for Data Analysis and decision-making in AI algorithms). ML is a specific approach within AI that uses algorithms to identify patterns in data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. This will ensure the data is in an ideal structure for further analysis.

AI 52

AI AI Data Lakes Database 52

Pickl AI

SEPTEMBER 18, 2024

Tools like pandas and SQL help manipulate and query data , while libraries such as matplotlib and Seaborn are used for data visualisation. Algorithm and Model Development Understanding various Machine Learning algorithms—such as regression , classification , clustering , and neural networks —is fundamental.

Machine Learning 52

Machine Learning Machine Learning Algorithm Natural Language Processing 52

DrivenData Labs

FEBRUARY 3, 2025

Recently, I became interested in machine learning, so I was enrolled in the Yandex School of Data Analysis and Computer Science Center. Machine learning is my passion and I often participate in competitions. To increase the amount of data, I tried to generate data using some LLMs in a few-shot way.

Machine Learning 130

Machine Learning Machine Learning Data Science Natural Language Processing 130

Dataconomy

MARCH 18, 2025

Anomaly detection is a critical component in the ever-evolving field of data analysis. In a world increasingly driven by data, the ability to identify outliers or unusual patterns can mean the difference between gaining valuable insights and missing crucial threats or opportunities.

Algorithm 91

Algorithm Machine Learning Machine Learning Clustering 91