Machine Learning
Top No-Code ML Platforms: ChatGPT Alternatives in 2025
We benchmarked 4 no-code machine learning platforms across key metrics: data processing (handling missing values, outliers), model setup and ease of use, accuracy metrics output, availability of visualizations, and any major limitations or notes observed during testing.
Machine Learning in Data Integration: 8 Use Cases & Challenges
Integrating and analyzing data from disparate sources effectively has become paramount. Data integration often presents challenges, ranging from managing AI data quality to ensuring security. As organizations grapple with these obstacles, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as transformative technologies, offering innovative solutions to simplify and enhance data integration processes.
Guide To Machine Learning Data Governance in 2025
In this article, we explain machine learning data governance. We explain its key principles, benefits, use cases, best practices, and our future expectations of data governance.
Sentiment Analysis Machine Learning: Approaches & 5 Examples
It is not surprising that the use of AI in the workplace has increased by 270% from 2015 to 2019, considering the data available and its exponential growth.
Multimodal Learning: How It Works & Real-Life Examples
Multimodal AI processes diverse data—audio, visual, and text—to deliver richer insights with greater accuracy. To maximize its value without overcommitting, businesses should assess their needs, data quality, and strategic goals. There are two distinct areas where multimodal learning is applied: machine learning and education.
30 Datasets for ML & AI Models in 2025
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. Explore different types of existing datasets: custom human-generated, custom machine-generated, natural language processing, open, public government, image, audio, and healthcare datasets to train your machine-learning models.
What are 5 Best Process Mining Algorithms to Consider?
Process mining algorithms are examples of how machine learning process mining applications can facilitate process discovery. TThey help clean the required data and generate process models with different strengths and weaknesses. Technical professionals and developers must decide which algorithm to use based on the data and models of the processes they want to automate.
Top 6 Applications of Machine Learning in Process Mining
For more than a decade, machine learning has been applied to traditional process mining . Today, many vendors claim to offer AI-powered process mining software which leverages machine learning.
TinyML(EdgeAI) in 2025: Machine Learning at the Edge
Applications of edge analytics transforming industries and the edge computing market is expected to reach ~$350 by 2027. However, the current approach to edge analytics involves machine learning models trained on the cloud. This introduces latency to the system and is prone to privacy issues.
![Web Scraping for Machine Learning: From HTML to ML ['25]](https://research.aimultiple.com/wp-content/uploads/2021/08/machine-learning-web-scraping-190x143.png.webp)
Web Scraping for Machine Learning: From HTML to ML ['25]
~54.7 billion people around the world have been recorded to use the internet, creating 1.7MB of data every second. Crawling this exponentially growing volume of data could provide many opportunities for breakthroughs in data science.