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. Learn about 101 ML algorithms for data science with cheat sheets 5.

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AWS Machine Learning Blog

MAY 8, 2024

They focused on improving customer service using data with artificial intelligence (AI) and ML and saw positive results, with their Group AI Maturity increasing from 50% to 80%, according to the TM Forum’s AI Maturity Index. million subscribers, which amounts to 57% of the Sri Lankan mobile market.

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ODSC - Open Data Science

OCTOBER 28, 2024

At the same time, massive breaches are exposing sensitive personally identifiable information (PII) at an unnerving rate. The main difference is the use of counterfeit information. The bad actor creates fake names, contact information, or biometric data instead of stealing every piece of PII they can.

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Dataconomy

MARCH 28, 2025

Feature selection is a critical component in the development of effective machine learning (ML) models. By systematically narrowing down the vast array of potential features, data analysts can enhance the model’s focus on the most informative elements.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. Machine learning(ML) is evolving at a very fast pace. Machine learning(ML) is evolving at a very fast pace. Models […]

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AWS Machine Learning Blog

MARCH 25, 2025

Key reasons include: Contextual coherence Maintaining state makes sure that the application can track the flow of information, leading to more coherent and contextually relevant outputs. Background State persistence in generative AI applications refers to the ability to maintain and recall information across multiple interactions.

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Pickl AI

APRIL 4, 2025

These tools enable data analysis, model building, and algorithm optimization, forming the backbone of ML applications. Introduction Machine Learning (ML) often seems like magic. Think of ML algorithms as sophisticated tools. Dimensionality reduction simplifies datasets while preserving critical information.

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Towards AI

SEPTEMBER 10, 2023

I’ve passed many ML courses before, so that I can compare. The course covers the basics of Deep Learning and Neural Networks and also explains Decision Tree algorithms. You start with the working ML model. Lesson #4: How to train large models on Kaggle Lots of beginners use Kaggle notebooks for ML.

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AWS Machine Learning Blog

JUNE 22, 2023

Light & Wonder teamed up with the Amazon ML Solutions Lab to use events data streamed from LnW Connect to enable machine learning (ML)-powered predictive maintenance for slot machines. Predictive maintenance is a common ML use case for businesses with physical equipment or machinery assets.

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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.

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Iguazio

APRIL 24, 2024

Generative AI is by no means a replacement for the previous wave of AI/ML (now sometimes referred to as ‘traditional AI/ML’), which continues to deliver significant value, and represents a distinct approach with its own advantages. In the end, we explain how MLOps can help accelerate the process and bring these models to production.

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Pickl AI

JANUARY 29, 2024

2024 Tech breakdown: Understanding Data Science vs ML vs AI Quoting Eric Schmidt , the former CEO of Google, ‘There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every two days.’ billion by 2032. billion in 2023 to an impressive $225.91

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Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. ML-based predictive models nowadays may consider time-dependent components — seasonality, trends, cycles, irregular components, etc. — to

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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. Definition of Supervised Learning and Unsupervised Learning Supervised learning is a process where an ML model is trained using labeled data. These are known as supervised learning and unsupervised learning.

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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? The information from previous decisions is analyzed via the decision tree.

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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? The information from previous decisions is analyzed via the decision tree.

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Flipboard

NOVEMBER 21, 2023

First, we extract features from a subset of the full dataset using the Diagnostic Feature Designer app, and then run the model training locally with a MATLAB decision tree model. You can set up and train a simple decision tree classifier locally. We start by training a classifier model on our desktop with MATLAB.

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Iguazio

JUNE 25, 2023

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in. Why Evaluate Model Performance?

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IBM Journey to AI blog

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses. DL, a subset of ML, excels at understanding context and generating human-like responses.

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Towards AI

APRIL 7, 2024

Additionally, the elimination of human loop processes has made it possible for AI/ML to construct training data for data annotation and labeling, which has a major influence on geospatial data. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data.

Machine Learning 102

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 102

Mlearning.ai

APRIL 12, 2023

With the emergence of machine learning (ML), developers now have an innovative approach for optimizing AngularJS performance. In this article, we’ll explore the concept of using ML to enhance AngularJS performance and provide practical tips for implementing ML strategies in your development process.

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The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

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Towards AI

APRIL 4, 2024

Machine learning (ML) has proven that it is here with us for the long haul, everyone who had their doubts by calling it a phase should by now realize how wrong they are, ML has being used in various sector’s of society such as medicine, geospatial data, finance, statistics and robotics.

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AWS Machine Learning Blog

SEPTEMBER 10, 2024

As part of its goal to help people live longer, healthier lives, Genomics England is interested in facilitating more accurate identification of cancer subtypes and severity, using machine learning (ML). We provide insights on interpretability, robustness, and best practices of architecting complex ML workflows on AWS with Amazon SageMaker.

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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. Classification In Classification, we use an ML Algorithm to classify the digit based on its features. Support Vector Machines (SVMs) are another ML models that can be used for HDR.

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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.

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Dataconomy

AUGUST 9, 2023

This is where the power of machine learning (ML) comes into play. Flow analysis tools like IPFIX, NetFlow, and sFlow collect flow data, which includes information about source and destination IPs, ports, and protocols. One of the primary applications of ML in network traffic analysis is anomaly detection.

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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. I wrote about Python ML here. Join thousands of data leaders on the AI newsletter.

Machine Learning 116

Machine Learning Machine Learning Algorithm K-nearest Neighbors 116

Google Research AI blog

FEBRUARY 7, 2023

In the last 10 years, AI and ML models have become bigger and more sophisticated — they’re deeper, more complex, with more parameters, and trained on much more data, resulting in some of the most transformative outcomes in the history of machine learning.

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Mlearning.ai

FEBRUARY 17, 2023

ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning. How is it actually looks in a real life process of ML investigation? In this article, I will cover all of them. Regression is a technique to predict the real continuous value. It’s a fantastic world, trust me!

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DrivenData Labs

MAY 22, 2024

This region faces dry conditions and high demand for water, and these forecasts are essential for making informed decisions. Vitaly Bondar: ML Team lead in theMind (formerly Neuromation) company with 6 years of experience in ML/AI and almost 20 years of experience in the industry.

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Pickl AI

SEPTEMBER 30, 2024

Introduction Machine Learning (ML) is revolutionising the business world by enabling companies to make smarter, data-driven decisions. As an advanced technology that learns from data patterns, ML automates processes, enhances efficiency, and personalises customer experiences. Data : Data serves as the foundation for ML.

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The MLOps Blog

DECEMBER 19, 2022

As Data Scientists, we all have worked on an ML classification model. In this article, we will talk about feasible techniques to deal with such a large-scale ML Classification model. In this article, you will learn: 1 What are some examples of large-scale ML classification models? Let’s take a look at some of them.

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Mlearning.ai

APRIL 4, 2023

In this blog, I will explain how financial institutions leverage ML to improve their credit risk models and its effect on the outcome. How Does ML Benefit Credit Risk Modelling? Expected Loss: EAD x PD x LGD This entire calculation can be offloaded to ML models, without compromising speed, accuracy or agility.

Machine Learning 52

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ML @ CMU

MAY 12, 2023

Unfortunately, while this data contains a wealth of useful information for disease forecasting, the data itself may be highly sensitive and stored in disparate locations (e.g., Graphs generally complicate DP : we are often used to ML settings where we can clearly define the privacy granularity and how it relates to an actual individual (e.g.

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Pickl AI

FEBRUARY 7, 2025

ML works with structured data, while DL processes complex, unstructured data. ML requires less computing power, whereas DL excels with large datasets. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two. What is Machine Learning?

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