Analytics Vidhya

AUGUST 5, 2019

Overview Evaluating a model is a core part of building an effective machine learning model There are several evaluation metrics, like confusion matrix, cross-validation, The post 11 Important Model Evaluation Metrics for Machine Learning Everyone should know appeared first on Analytics Vidhya.

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Data Science Dojo

JULY 5, 2024

Machine learning models are algorithms designed to identify patterns and make predictions or decisions based on data. Modern businesses are embracing machine learning (ML) models to gain a competitive edge. What is Machine Learning Model Testing? What is the Difference between Model Evaluation and Testing?

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KDnuggets

AUGUST 6, 2019

Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.

Python 301

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Pickl AI

DECEMBER 5, 2024

Summary: Cross-validation in Machine Learning is vital for evaluating model performance and ensuring generalisation to unseen data. Introduction In this article, we will explore the concept of cross-validation in Machine Learning, a crucial technique for assessing model performance and generalisation.

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Pickl AI

MARCH 5, 2025

Summary: Accuracy in Machine Learning measures correct predictions but can be deceptive, particularly with imbalanced or multilabel data. Introduction When you work with Machine Learning , accuracy is the easiest way to measure success. Key Takeaways: Accuracy in Machine Learning is a widely used metric.

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Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices.

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SAS Software

MAY 23, 2025

Using SAS Viya Workbench for efficient setup and execution, this beginner-friendly guide shows how Scikit-learn pipelines can streamline machine learning workflows and prevent common errors. The post Python ML pipelines with Scikit-learn: A beginners guide appeared first on SAS Blogs.

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Data Science Dojo

AUGUST 24, 2023

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

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Data Science Dojo

AUGUST 19, 2024

These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential. What is the bias-variance trade-off, and how do you address it in machine learning models?

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Mlearning.ai

JANUARY 27, 2023

Understand Different Techniques and How to Use Them for Better Model Evaluation Photo by Kelly Sikkema on Unsplash We develop machine-learning models from data. How we do this is the subject of the concept of cross-validation. Diagram of k-fold cross-validation. Train-test split. Image by the author.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Common ML libraries such as OpenCV or scikit-learn are also used to perform crop segmentation using KNN classification, and these are also installed in the geospatial kernel.

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Dataconomy

MARCH 27, 2025

Scikit-learn stands out as a prominent Python library in the machine learning realm, providing a versatile toolkit for data scientists and enthusiasts alike. Its comprehensive functionality caters to various tasks, making it a go-to resource for both simple and complex machine learning projects.

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How to Learn Machine Learning

MAY 21, 2025

Welcome back to another exciting journey through the Machine Learning landscape! In our Machine Learning journey, we often fixate on metrics like accuracy, precision, and recall. Implementing the Brier Score in Python Enough theory let’s get our hands dirty with some Python ! Hello friend!

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Mlearning.ai

FEBRUARY 2, 2023

Data scientists use a technique called cross validation to help estimate the performance of a model as well as prevent the model from… Continue reading on MLearning.ai »

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Pickl AI

JUNE 4, 2024

Summary : Building a machine learning model is just one step. Validating its performance on unseen data is crucial. Python offers various tools like train-test split and cross-validation to assess model generalizability. This helps identify overfitting and select the best model for real-world use.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation.

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Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. The global Machine Learning market was valued at USD 35.80

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Pickl AI

FEBRUARY 19, 2025

Summary: Boosting in Machine Learning improves predictive accuracy by sequentially training weak models. Introduction Boosting in Machine Learning is a powerful ensemble technique. You will also learn about its advantages and real-world applications. This blog explores how Boosting works and its popular algorithms.

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Pickl AI

SEPTEMBER 18, 2024

Summary: Machine Learning Engineer design algorithms and models to enable systems to learn from data. Introduction Machine Learning is rapidly transforming industries. A Machine Learning Engineer plays a crucial role in this landscape, designing and implementing algorithms that drive innovation and efficiency.

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

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Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Unlocking the Power of KNN Algorithm in Machine Learning Machine learning algorithms are significantly impacting diverse fields.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

By following this data-driven approach, the classifier can accurately categorize new inputs based on their similarity to the learned characteristics of each class, capturing the nuances and diversity within each category. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.

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Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season.

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Mlearning.ai

JUNE 7, 2023

Machine learning is a rapidly evolving field that provides powerful tools for data analysis and prediction. Continue reading on MLearning.ai »

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Pickl AI

MAY 24, 2023

Introduction One of the most widely used and highly popular programming languages in the technological world is Python. Significantly, despite being user-friendly and easy to learn, one of Python’s many advantages is that it has large collection of libraries. What is a Python Library? What version of Python are you using?

Data Science 52

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Pickl AI

AUGUST 8, 2024

Summary: Support Vector Machine (SVM) is a supervised Machine Learning algorithm used for classification and regression tasks. Introduction Machine Learning has revolutionised various industries by enabling systems to learn from data and make informed decisions. What is the SVM Algorithm in Machine Learning?

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. Machine learning yearning. References [1].Ng, Ng, Andrew.

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Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. Let’s get started!

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Mlearning.ai

JUNE 28, 2023

{This article was written without the assistance or use of AI tools, providing an authentic and insightful exploration of PyCaret} Image by Author ‍In the rapidly evolving realm of data science, the imperative to automate machine learning workflows has become an indispensable requisite for enterprises aiming to outpace their competitors.

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AWS Machine Learning Blog

OCTOBER 3, 2024

Steamlining model management and deployment with SageMaker Amazon SageMaker is a managed machine learning platform that provides data scientists and data engineers familiar concepts and tools to build, train, deploy, govern , and manage the infrastructure needed to have highly available and scalable model inference endpoints.

Data Science 91

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. What are kernels? Linear Kernels 2.

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Ocean Protocol

FEBRUARY 2, 2023

Indeed, the most robust predictive trading algorithms use machine learning (ML) techniques. Moving on to the fun stuff… Setting up our environment First, we’ll set up our environment with a Prophet machine learning model to forecast prices. It’s time to use machine learning to forecast prices. Easy peasy.

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Mlearning.ai

MARCH 15, 2023

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Data Extraction, Preprocessing & EDA : Extract & Pre-process the data using Python and perform basic Exploratory Data Analysis.

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Mlearning.ai

FEBRUARY 23, 2023

Perceptron Implementation in Python: Understanding the Basics of Artificial Neural Networks Photo by Jeremy Perkins on Unsplash Perceptron is the most basic unit of an artificial neural network. Python Let’s code a perceptron in Python. It takes several inputs and outputs a single binary decision. A Perceptron.

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Heartbeat

FEBRUARY 20, 2023

First-time project and model registration Photo by Isaac Smith on Unsplash The world of machine learning and data science is awash with technicalities. Machine learning problems could grow to such an extent that you constantly lose track of what you are doing. One problem that is particularly prevalent is model tracking.

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Pickl AI

JANUARY 28, 2025

Use cross-validation and regularisation to prevent overfitting and pick an appropriate polynomial degree. You can detect and mitigate overfitting by using cross-validation, regularisation, or carefully limiting polynomial degrees. It offers flexibility for capturing complex trends while remaining interpretable.

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Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Explain the bias-variance tradeoff in Machine Learning. Here is a brief description of the same.

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