Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

Flipboard

NOVEMBER 21, 2023

We have access to a large repository of labeled data generated from a Simulink simulation that has three possible fault types in various possible combinations (for example, one healthy and seven faulty states). The model can be tuned to match operational data from our real pump using parameter estimation techniques in MATLAB and Simulink.

Machine Learning 166

Machine Learning Machine Learning AWS Decision Trees 166

Ocean Protocol

NOVEMBER 28, 2024

Using innovative approaches and advanced algorithms, participants modeled scenarios accounting for starting grid positions, driver performance, and unpredictable race conditions like weather changes or mid-race interruptions. His focus on track-specific insights and comprehensive data preparation set the model apart.

Cross Validation 85

Cross Validation Decision Trees Data Scientist Data Science 85

PyImageSearch

OCTOBER 21, 2024

One such technique is the Isolation Forest algorithm, which excels in identifying anomalies within datasets. In this tutorial, you will learn how to implement a predictive maintenance system using the Isolation Forest algorithm — a well-known algorithm for anomaly detection. And Why Anomaly Detection?

Algorithm 110

Algorithm Deep Learning Deep Learning Data Preparation 110

Pickl AI

JULY 12, 2024

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. It involves algorithms that identify and use data patterns to make predictions or decisions based on new, unseen data.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

DECEMBER 12, 2024

Summary: XGBoost is a highly efficient and scalable Machine Learning algorithm. It combines gradient boosting with features like regularisation, parallel processing, and missing data handling. Key Features of XGBoost XGBoost (eXtreme Gradient Boosting) has earned its reputation as a powerful and efficient Machine Learning algorithm.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

FEBRUARY 4, 2024

Alteryx’s Capabilities Data Blending: Effortlessly combine data from multiple sources. Predictive Analytics: Leverage machine learning algorithms for accurate predictions. This makes Alteryx an indispensable tool for businesses aiming to glean insights and steer their decisions based on robust data.

Data Preparation 52

Data Preparation Tableau Data Visualization Analytics 52

ODSC - Open Data Science

OCTOBER 7, 2024

It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning. Model Training: Running computations to learn from the data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

OCTOBER 3, 2024

Summary: Predictive analytics utilizes historical data, statistical algorithms, and Machine Learning techniques to forecast future outcomes. This blog explores the essential steps involved in analytics, including data collection, model building, and deployment. What is Predictive Analytics?

Predictive Analytics 52

Predictive Analytics Analytics Analytics Machine Learning 52

AWS Machine Learning Blog

JUNE 22, 2023

Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering. Data preparation To extract data efficiently for training and testing, we utilize Amazon Athena and the AWS Glue Data Catalog.

AWS 111

AWS ML ML Machine Learning 111

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This results in quite efficient sales data predictions. In its core, lie gradient-boosted decision trees.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. These nuanced algorithms can lead to more accurate and reliable credit scores and decisions. loan default or not).

ML 52

ML ML Machine Learning Machine Learning 52

AWS Machine Learning Blog

OCTOBER 8, 2024

SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment. Data preparation The foundation of any machine learning project is data preparation.

Machine Learning 95

Machine Learning Machine Learning Data Preparation AWS 95

Pickl AI

NOVEMBER 12, 2024

They provide a comprehensive environment for designing algorithms, simulating their performance, and generating code for deployment on various hardware platforms. Simulation Capabilities: Users can simulate AI algorithms within their models to evaluate performance before deployment. Model Selection : Choose appropriate algorithms (e.g.,

AI 52

AI AI Deep Learning Deep Learning 52

Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

NOVEMBER 5, 2024

Domain knowledge is crucial for effective data application in industries. What is Data Science and Artificial Intelligence? Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

OCTOBER 15, 2024

They identify patterns in existing data and use them to predict unknown events. Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. Popular clustering algorithms include k-means and hierarchical clustering. Data preparation also involves feature engineering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Pickl AI

MAY 30, 2024

You will collect and clean data from multiple sources, ensuring it is suitable for analysis. You will perform Exploratory Data Analysis to uncover patterns and insights hidden within the data. This phase entails meticulously selecting and training algorithms to ensure optimal performance.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

The MLOps Blog

DECEMBER 19, 2022

Lesson 1: Mitigating data sparsity problems within ML classification algorithms What are the most popular algorithms used to solve a multi-class classification problem? So, the model might not have a sufficient number of data samples to learn the pattern for each class. Let’s take a look at some of them.

ML 52

ML ML Algorithm Deep Learning 52

Pickl AI

DECEMBER 4, 2024

The Current State of Data Science Data Science today is characterised by its integration with various technologies and methodologies that enhance its capabilities. The field has evolved significantly from traditional statistical analysis to include sophisticated Machine Learning algorithms and Big Data technologies.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD. It is designed to leverage hardware acceleration (e.g.,

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Towards AI

JULY 19, 2023

Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. Data Preparation Photo by Bonnie Kittle […]

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Dataconomy

MARCH 28, 2025

Machine learning algorithms represent a transformative leap in technology, fundamentally changing how data is analyzed and utilized across various industries. What are machine learning algorithms? Regression: Focuses on predicting continuous values, such as forecasting sales or estimating property prices.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Dataconomy

MARCH 18, 2025

CatBoost is quickly becoming a go-to algorithm in the machine learning landscape, particularly for its innovative approach to handling categorical data. The algorithms efficacy in small datasets and rapid training capabilities set it apart from other models, particularly in scenarios involving categorical features.

Decision Trees 91

Decision Trees Machine Learning Machine Learning Algorithm 91

How to Learn Machine Learning

MARCH 25, 2025

It uses unlabeled data where only inputs are given without any predefined outputs. The ML algorithm tries to find hidden patterns and structures in this data. It groups similar data points or identifies outliers without prior guidance. Unsupervised learning deals with data that has not been labeled.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59