Data Science Dojo

MAY 27, 2024

Its discriminative AI capabilities allow it to analyze audio inputs, extract relevant information, and generate appropriate responses, showcasing the power of AI-driven conversational systems in enhancing user experiences and streamlining business operations.

K-nearest Neighbors 338

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Dataconomy

APRIL 16, 2025

Definition of supervised learning At its core, supervised learning utilizes labeled data to inform a machine learning model. Common algorithms used in classification tasks include: Decision Trees: A tree-like model that makes decisions based on feature values.

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Supervised Learning Decision Trees Algorithm Machine Learning 91

Data Science Dojo

AUGUST 11, 2023

Throughout the course of history, the significance of creating and disseminating information has been immensely crucial. Moreover, statistical inference empowers them to make informed decisions and draw meaningful conclusions based on sample data. Decision trees are used to classify data into different categories.

Data Science 195

Data Science Python Data Scientist Decision Trees 195

Flipboard

MAY 25, 2025

Feature selection via the Boruta and LASSO algorithms preceded the construction of predictive models using Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, LightGBM, and XGBoost.

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K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 77

Dataconomy

MARCH 28, 2025

Their application spans a wide array of tasks, from categorizing information to predicting future trends, making them an essential component of modern artificial intelligence. What are machine learning algorithms? Decision trees: They segment data into branches based on sequential questioning.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Data Science Dojo

JULY 23, 2024

With the growing use of machine learning (ML) models to handle, store, and manage data, the efficiency and impact of enterprises have also increased. Categorical data is one such form of information that is handled by ML models using different methods. This conversion allows models to process the data and extract valuable information.

Machine Learning 195

Machine Learning Machine Learning Algorithm ML 195

Data Science Dojo

APRIL 10, 2023

Data mining can help governments identify areas of concern, allocate resources, and make informed policy decisions. In data mining, popular algorithms include decision trees, support vector machines, and k-means clustering.

Data Mining 195

Data Mining Data Mining Data Mining Algorithm 195

Pickl AI

JANUARY 12, 2025

Develop Hybrid Models Combine traditional analytical methods with modern algorithms such as decision trees, neural networks, and support vector machines. Such an organisation that employs these practices would learn to make improved and well-informed decisions to stay competitive.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 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

Towards AI

JULY 15, 2024

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code. Radom Forest install.packages("randomForest")library(randomForest) 4. data = trainData) 5.

Machine Learning 116

Machine Learning Machine Learning Algorithm K-nearest Neighbors 116

Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Introduction Machine Learning (ML) has rapidly evolved over the past few years, becoming an integral part of various industries, from healthcare to finance.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Towards AI

APRIL 11, 2023

Feature Extraction Feature Extraction is basically extracting relevant information from the pre-processed image that can be used for classification. ANNs consist of layers of interconnected nodes, which process and transmit information. Support Vector Machines (SVMs) are another ML models that can be used for HDR.

Machine Learning 98

Machine Learning Machine Learning Support Vector Machines Algorithm 98

Dataconomy

AUGUST 31, 2023

The classification model learns from the training data, identifying the distinguishing characteristics between each class, enabling it to make informed predictions. Classification in machine learning can be a versatile tool with numerous applications across various industries. Next, you need to select a model.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Towards AI

FEBRUARY 20, 2024

Common Machine Learning Algorithms Machine learning algorithms are not limited to those mentioned below, but these are a few which are very common. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., Models […]

Machine Learning 103

Machine Learning Machine Learning ML ML 103

How to Learn Machine Learning

MARCH 25, 2025

One relies on structured, labeled information to make predictions, while the other uncovers hidden patterns in raw data. Understanding their differences is essential for businesses looking to implement machine learning effectively. The model uses this information to learn the relationship between input and output.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59

Towards AI

APRIL 14, 2023

Adding such extra information should improve the classification compared to the previous method (Principle Label Space Transformation). Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Dataconomy

OCTOBER 5, 2023

Data preprocessing tasks can include data cleaning to remove errors or inconsistencies, normalization to bring data within a consistent range, and feature engineering to extract meaningful information from raw data. AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand.

AI 136

AI AI Artificial Intelligence Artificial Intelligence 136

Pickl AI

AUGUST 1, 2023

Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks. Common examples include: Linear Regression: It is the best Machine Learning model and is used for predicting continuous numerical values based on input features.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Dataconomy

JUNE 13, 2023

Making the right decisions in an aggressive market is crucial for your business growth and that’s where decision intelligence (DI) comes to play. In this era of information overload, utilizing the power of data and technology has become paramount to drive effective decision-making. What is decision intelligence?

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Power BI Artificial Intelligence Data Analysis Data Analysis 103

Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

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Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Mlearning.ai

FEBRUARY 2, 2023

In the context of the MAB algorithm, each arm represents a decision that can be taken, and the reward corresponds to some measure of performance or utility. bag of words or TF-IDF vectors) and splitting the data into training and testing sets.

K-nearest Neighbors 52

K-nearest Neighbors Algorithm Support Vector Machines Decision Trees 52

Pickl AI

JULY 1, 2024

Demand forecasting , the art of anticipating customer needs, allows companies to optimize inventory levels, streamline production processes, and make informed strategic decisions. Data Transformation Combine existing data points to create features that might be more informative for forecasting.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Mlearning.ai

FEBRUARY 17, 2023

Simple linear regression Multiple linear regression Polynomial regression Decision Tree regression Support Vector regression Random Forest regression Classification is a technique to predict a category. It’s a fantastic world, trust me!

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Pickl AI

DECEMBER 9, 2024

In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds. Inductive bias is crucial in ensuring that Machine Learning models can learn efficiently and make reliable predictions even with limited information by guiding how they make assumptions about the data.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

IBM Journey to AI blog

DECEMBER 19, 2023

Similar to a “ random forest ,” it creates “decision trees,” which map out the data points and randomly select an area to analyze. Isolation forest models can be found on the free machine learning library for Python, scikit-learn.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Pickl AI

JULY 12, 2024

NLP tasks include machine translation, speech recognition, and sentiment analysis. Computer Vision This is a field of computer science that deals with the extraction of information from images and videos. EDA guides subsequent preprocessing steps and informs the selection of appropriate AI algorithms based on data insights.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 31, 2023

Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Dataconomy

AUGUST 9, 2023

Flow analysis tools like IPFIX, NetFlow, and sFlow collect flow data, which includes information about source and destination IPs, ports, and protocols. The stack allows for customizable data visualization, enabling you to create informative dashboards and reports to understand network behaviors.

Machine Learning 91

Machine Learning Machine Learning ML ML 91

Heartbeat

APRIL 17, 2023

One of the goals of ML is to enable computers to process and analyze data in a way that is similar to how humans process information. Human brains are capable of processing vast amounts of information from the environment and making complex decisions based on that information. synonyms).

Natural Language Processing 52

Natural Language Processing Support Vector Machines Machine Learning Machine Learning 52

phData

AUGUST 24, 2023

This creditworthiness is influenced by several key factors: Credit History: The primary source of information is usually the applicant’s credit history, which is a detailed record of all past borrowing and repayment, including late payments and defaults. This is where credit-decisioning models are essential.

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

OCTOBER 15, 2024

It encompasses various models and techniques, applicable across industries like finance and healthcare, to drive informed decision-making. Introduction Statistical Modeling is crucial for analysing data, identifying patterns, and making informed decisions. Model selection requires balancing simplicity and performance.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Pickl AI

DECEMBER 10, 2024

Model-Related Hyperparameters Model-related hyperparameters are specific to the architecture and structure of a Machine Learning model. They vary significantly between model types, such as neural networks , decision trees, and support vector machines.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

SEPTEMBER 12, 2024

By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success. Data Science is the art and science of extracting valuable information from data.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

Heartbeat

SEPTEMBER 28, 2023

Customer Feedback: Understanding why customers leave provides valuable information to improve your service. Model stacking involves training multiple machine learning models and using another model to combine their predictions to improve accuracy. Random Forest Classifier (rf): Ensemble method combining multiple decision trees.

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