Pickl AI

SEPTEMBER 16, 2024

Support Vector Machines (SVM) Support Vector Machines are powerful supervised learning algorithms used for classification and regression tasks. Natural Language Processing: Understanding and generating human language. Applications Image Recognition: Identifying objects in images.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 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

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. appeared first on IBM Blog.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

APRIL 9, 2025

AI Drives Automation and Efficiency : Improves processes across industries. DL Enhances Predictive Analytics: Excels in image and speech recognition tasks. Language Understanding: Processing and interpreting human language (Natural Language Processing – NLP).

Deep Learning 52

Deep Learning Deep Learning Artificial Intelligence Artificial Intelligence 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

IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. 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.

Machine Learning 68

Machine Learning Machine Learning Data Mining Data Mining 68

Pickl AI

SEPTEMBER 30, 2024

Key concepts in ML are: Algorithms : Algorithms are the mathematical instructions that guide the learning process. They process data, identify patterns, and adjust the model accordingly. Common algorithms include decision trees, neural networks, and support vector machines.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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. Scikit-learn also earns a top spot thanks to its success with predictive analytics and general machine learning.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. After that, move towards unsupervised learning methods like clustering and dimensionality reduction.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

Healthcare Data Science is revolutionising healthcare through predictive analytics, personalised medicine, and disease detection. Data Science continues to impact various industries, driving innovation and efficiency through data-driven insights and advanced analytics.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 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. Deep Learning Deep Learning is a specialised subset of Machine Learning involving multi-layered neural networks to solve complex problems. They are handy for high-dimensional data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

NOVEMBER 18, 2024

Common Applications of Machine Learning Machine Learning has numerous applications across industries. Predictive analytics uses historical data to forecast future trends, such as stock market movements or customer churn. The algorithm you select depends on the nature of the problem and the type of data you have.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

SEPTEMBER 12, 2024

Normalisation: The process of scaling individual data points to a common range, often used to improve the performance of Machine Learning algorithms. Natural Language Processing (NLP): A field of Artificial Intelligence that focuses on the interaction between computers and human language.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

PyImageSearch

JANUARY 2, 2023

Companies are tackling the weakness in data interoperability by cleaning and structuring data and overlaying analytics to make meaningful predictions to improve health. Because of its sensitive nature, managing mental health is more effective when the person receiving care interacts with the healthcare provider.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

DrivenData Labs

MAY 1, 2025

Advance algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions. Some participants combined a transformer neural network with a tree-based model or support vector machine (SVM). Phase Description Phase 1 [Find IT!] Phase 2 [Build IT!]

Decision Trees 147

Decision Trees Clustering Algorithm Machine Learning 147