Dataconomy

AUGUST 31, 2023

The goal of unsupervised learning is to identify structures in the data, such as clusters, dimensions, or anomalies, without prior knowledge of the expected output. Some popular classification algorithms include logistic regression, decision trees, random forests, support vector machines (SVMs), and neural networks.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Pickl AI

JULY 12, 2024

Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents. Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. classification, regression) and data characteristics.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

JUNE 13, 2023

It leverages the power of technology to provide actionable insights and recommendations that support effective decision-making in complex business scenarios. At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs.

Power BI 103

Power BI Artificial Intelligence Artificial Intelligence Data Analysis 103

Mlearning.ai

JUNE 8, 2023

The resulting model can then be used to classify new documents based on their content. Sentence embeddings can also be used in text classification by representing entire sentences as high-dimensional vectors and then feeding them into a classifier. Clustering  — we can cluster our sentences, useful for topic modeling.

Machine Learning 52

Machine Learning Machine Learning Clustering Database 52

Pickl AI

OCTOBER 15, 2024

Applications : Stock price prediction and financial forecasting Analysing sales trends over time Demand forecasting in supply chain management Clustering Models Clustering is an unsupervised learning technique used to group similar data points together. Popular clustering algorithms include k-means and hierarchical clustering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Pickl AI

NOVEMBER 28, 2024

Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

SEPTEMBER 12, 2024

C Classification: A supervised Machine Learning task that assigns data points to predefined categories or classes based on their characteristics. Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

Well-supported: Python has a large community of followers that includes professionals from the academic and industrial circles which allows them to use the analytics libraries for problem solving. Accordingly, it is possible for the Python users to ask for help from Stack Overflow, mailing lists and user-contributed code and documentation.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

DagsHub

APRIL 21, 2024

Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques. It is commonly used in MLOps workflows for deploying and managing machine learning models and inference services.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

ODSC - Open Data Science

JANUARY 7, 2025

Natural Language Processing (NLP) has emerged as a dominant area, with tasks like sentiment analysis, machine translation, and chatbot development leading the way. Classification techniques, such as image recognition and document categorization, remain essential for a wide range of industries.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

DagsHub

DECEMBER 23, 2024

  Image classification Text categorization Document sorting Sentiment analysis Medical image diagnosis Advantages Pool-based active learning can leverage relationships between data points through techniques like density-based sampling and cluster analysis.   Supports batch processing for quick processing for the images. 

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52