Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., edges, corners, or color histograms).

Machine Learning 123

Machine Learning Machine Learning Deep Learning Deep Learning 123

Data Science Blog

OCTOBER 20, 2024

This allows for a holistic view of the customer journey, including post-conversion events like returns and cancellations, which are crucial for accurate attribution modeling. It calculates and assigns credit to the marketing touchpoints that have influenced a desired business outcome for a specific key performance indicator (KPI) event.

Machine Learning 130

Machine Learning Machine Learning Deep Learning Deep Learning 130

Dataconomy

OCTOBER 5, 2023

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. With the model selected, the initialization of parameters takes place.

AI 136

AI AI Artificial Intelligence Artificial Intelligence 136

Dataconomy

MAY 25, 2023

By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data. This enables them to respond quickly to changing conditions or events. Deep learning, in combination with IoT, unlocks various possibilities.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

AssemblyAI

MAY 2, 2023

Discriminative AI (left) finds a conditional distribution or decision boundary in the space, whereas Generative AI (right) models the joint distribution Aside - conditional and joint distributions Conditional distributions give the probability of different events occurring conditioned on a fact.

AI 129

AI AI Support Vector Machines Artificial Intelligence 129

Heartbeat

MAY 22, 2023

Schematic diagram of the overall framework of Emotion Recognition System [ Source ] The models that are used for AI emotion recognition can be based on linear models like Support Vector Machines (SVMs) or non-linear models like Convolutional Neural Networks (CNNs).

AI 52

AI AI Deep Learning Deep Learning 52

ODSC - Open Data Science

JANUARY 5, 2024

If you’re looking to start building up your skills in these important Python libraries, especially for those that are used in machine & deep learning, NLP, and analytics, then be sure to check out everything that ODSC East has to offer. Interested in attending an ODSC event? And did any of your favorites make it in?

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

IBM Journey to AI blog

DECEMBER 19, 2023

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements. But what is an anomaly and why is detecting it important?

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Heartbeat

APRIL 17, 2023

NRE is a complex task that involves multiple steps and requires sophisticated machine learning algorithms like Hidden Markov Models (HMMs) , Conditional Random Fields (CRFs), and Support Vector Machines (SVMs) be present. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Natural Language Processing 52

Natural Language Processing Support Vector Machines Machine Learning Machine Learning 52

Heartbeat

MARCH 21, 2023

Hinge Losses  — Another set of losses for classification problems, but commonly used in support vector machines. The sequential model API allows you to create a deep learning model where the sequential class is created, and then you add layers to it. Here we’re building a sequential model.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Heartbeat

FEBRUARY 15, 2024

In the ever-evolving realm of artificial intelligence, computer vision is a crucial discipline that enables machines to interpret and glean insights from visual data. This learning process enables the system to make accurate predictions. One such powerful approach that has proven its worth is the Histogram of Oriented Gradients (HOG).

Deep Learning 40

Deep Learning Deep Learning Support Vector Machines ML 40

Heartbeat

SEPTEMBER 28, 2023

Model Training We train multiple machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine. Support Vector Machine (svm): Versatile model for linear and non-linear data. These models serve as the basis for our ensemble approach.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

Heartbeat

OCTOBER 23, 2023

By analyzing historical data and utilizing predictive machine learning algorithms like BERT, ARIMA, Markov Chain Analysis, Principal Component Analysis, and Support Vector Machine, they can assess the likelihood of adverse events, such as hospital readmissions, and stratify patients based on risk profiles.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Heartbeat

JUNE 14, 2023

Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. Support vector machine classifiers as applied to AVIRIS data.” Measuring Calibration in Deep Learning. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Deep Learning 59

Deep Learning Deep Learning Support Vector Machines Machine Learning 59

ODSC - Open Data Science

JANUARY 7, 2025

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. Deep Learning Deep learning is a cornerstone of modern AI, and its applications are expanding rapidly.

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

Heartbeat

OCTOBER 25, 2023

Machine Learning Tools in Bioinformatics Machine learning is vital in bioinformatics, providing data scientists and machine learning engineers with powerful tools to extract knowledge from biological data. Deep learning, a subset of machine learning, has revolutionized image analysis in bioinformatics.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

Heartbeat

MARCH 6, 2024

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. We're committed to supporting and inspiring developers and engineers from all walks of life.

Exploratory Data Analysis 52

Exploratory Data Analysis Support Vector Machines Data Analysis Data Analysis 52

Pickl AI

SEPTEMBER 3, 2023

Observations that deviate from the majority of the data are known as anomalies and might take the shape of occurrences, trends, or events that differ from customary or expected behaviour. Finding anomalous occurrences that might point to intriguing or potentially significant events is the aim of anomaly detection.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Heartbeat

AUGUST 22, 2023

It has been used to train and test a variety of machine learning models, including artificial neural networks, convolutional neural networks, and support vector machines, among others. We’re committed to supporting and inspiring developers and engineers from all walks of life. You can get the dataset here.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Support Vector Machines 52

Dataconomy

FEBRUARY 24, 2023

Several algorithms are available, including decision trees, neural networks, and support vector machines. The field of computer science known as “artificial intelligence” (AI) focuses on creating intelligent machines that can accomplish jobs that would normally need human intelligence.

Artificial Intelligence 91

Artificial Intelligence Artificial Intelligence AI AI 91

Pickl AI

JULY 1, 2024

Support Vector Machines (SVMs) SVMs create a hyperplane to separate different data classes, helping predict future demand based on historical patterns. Ensemble Learning Combine multiple forecasting models (e.g., Unexpected events or significant changes in market conditions can impact the accuracy of forecasts.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

Another example can be the algorithm of a support vector machine. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. What is deep learning?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis. Students should understand the concepts of event-driven architecture and stream processing. Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

phData

OCTOBER 21, 2024

In this blog, we discuss LLMs and how they fall under the umbrella of AI and Machine learning. Large Language Models are deep learning models that recognize, comprehend, and generate text, performing various other natural language processing (NLP) tasks. What Are Large Language Models?

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

DagsHub

DECEMBER 23, 2024

Key Characteristics Static Dataset : Works with a predefined set of unlabeled examples Batch Selection : Can select multiple samples simultaneously for labeling because of which it is widely used by deep learning models. Pool-Based Active Learning Scenario : Classifying images of artwork styles for a digital archive.  

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 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 55

Clustering Algorithm Machine Learning Machine Learning 55

Heartbeat

MARCH 8, 2023

The e1071 package provides a suite of statistical classification functions, including support vector machines (SVMs), which are commonly used for spam detection. Naive Bayes, according to Nagesh Singh Chauhan in KDnuggets, is a straightforward machine learning technique that uses Bayes’ theorem to create predictions.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Mining 52

Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Dataconomy

FEBRUARY 26, 2025

Meteorological software In weather forecasting, pattern recognition helps analyze historical data to predict future weather events. Further exploration Several related topics warrant further consideration: Comparative analysis: Deep learning and machine learning each have unique approaches toward pattern recognition.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Decision Trees 91