Towards AI

AUGUST 23, 2024

Like regular ML, LLM hyperparameters (e.g., The evaluation process should mirror standard machine learning practices; using train-test-validation splits or k-fold cross-validation, finding an updated version and evaluating it on the keep aside population. temperature or model version) should be logged as well.

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DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Data Science?

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DrivenData Labs

MAY 22, 2024

Unlike typical data science competitions, there's no predefined training dataset provided. This means participants must not only focus on modeling but also on finding the right data to be used. Forecast skill will be evaluated in August when the ground truth data becomes available.

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Pickl AI

MAY 24, 2023

To help you understand Python Libraries better, the blog will explain a Python Libraries for Data Science List which you can learn about. This may include for instance in Machine Learning, Data Science, Data Visualisation, image and Data Manipulation. What is a Python Library?

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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. Comet ML has an intricate web of tools that combine simplicity and safety and allows one to not only track changes in their model but also deploy them as desired or shared in teams.

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Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. Now comes the exciting part ….

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AWS Machine Learning Blog

DECEMBER 13, 2024

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. Ensemble models are becoming popular within the ML communities. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models.

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Towards AI

AUGUST 23, 2024

Simplifying LLM Development: Treat It Like Regular ML Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Like regular ML, LLM hyperparameters (e.g., temperature or model version) should be logged as well.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module. To implement the classifier, we employed a classic ML algorithm, SVM, using the scikit-learn Python module. The aim is to understand which approach is most suitable for addressing the presented challenge.

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DrivenData Labs

JANUARY 11, 2023

The results of this GCMS challenge could not only support NASA scientists to more quickly analyze data, but is also a proof-of-concept of the use of data science and machine learning techniques on complex GCMS data for future missions. I teach computer programming, data science and software engineering courses.

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Ocean Protocol

SEPTEMBER 29, 2023

This data challenge took NFL player performance data and fantasy points from the last 6 seasons to calculate forecasted points to be scored in the 2024 NFL season that began Sept. AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics.

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Pickl AI

NOVEMBER 28, 2024

Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. Fundamental Programming Skills Strong programming skills are essential for success in ML.

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Pickl AI

MAY 17, 2023

Training data plays an important role in deciding the effectiveness of an ML model. In the case of underfitting training data, the model is not able to establish a correlation between the input and output variables. It means that the statistical model fits closely against the training data. Thus, impacting the output.

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Mlearning.ai

FEBRUARY 3, 2023

Example: Think of the ML model as a robot that you want to teach how to do a specific task, like recognizing animals. Parameters are values that are learned by an ML model during the training process, while Hyperparameters are set prior to training and remain constant during the training process.

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The MLOps Blog

DECEMBER 7, 2022

And we at deployr , worked alongside them to find the best possible answers for everyone involved and build their Data and ML Pipelines. Building data and ML pipelines: from the ground to the cloud It was the beginning of 2022, and things were looking bright after the lockdown’s end.

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Ocean Protocol

FEBRUARY 1, 2024

Challenge Overview Objective : Building upon the insights gained from Exploratory Data Analysis (EDA), participants in this data science competition will venture into hands-on, real-world artificial intelligence (AI) & machine learning (ML). You can download the dataset directly through Desights.

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Mlearning.ai

SEPTEMBER 24, 2023

Figure 1: Brute Force Search It is a cross-validation technique. It trains several models using k — 1 of the folds as training data. The remaining fold is used as test data to compute a performance measure. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. 2019) Data Science with Python.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Each season consists of around 17,000 plays.

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DrivenData Labs

APRIL 10, 2024

Michal Wierzbinski ¶ Place: 2nd Place Prize: $3,000 Hometown: Rabka-Zdroj (near the city of Cracow), Poland Username: xultaeculcis Social Media: GitHub , LinkedIn Background: ML Engineer specializing in building Deep Learning solutions for Geospatial industry in a cloud native fashion. What motivated you to compete in this challenge?

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Heartbeat

OCTOBER 31, 2023

Cross-validation is recommended as best practice to provide reliable results because of this. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Pickl AI

MARCH 26, 2024

Experimentation and cross-validation help determine the dataset’s optimal ‘K’ value. Distance Metrics Distance metrics measure the similarity between data points in a dataset. Cross-Validation: Employ techniques like k-fold cross-validation to evaluate model performance and prevent overfitting.

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Heartbeat

MARCH 27, 2023

Model versioning and tracking with Comet ML Photo by Maxim Hopman on Unsplash In the first part of this article , we made a point to go through the steps that are necessary for you to log a model into the registry. This was necessary as the registry is where a machine learning practitioner can keep track of experiments and model versions.

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Ocean Protocol

AUGUST 8, 2023

We are excited to announce the winners of the first-ever invite-only data challenge hosted by Ocean Protocol! We received great feedback when tasked our data science community with the original sentiment analysis of the OCEAN token challenge, and now are able to share results from the second leg of this frontier.

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Mlearning.ai

NOVEMBER 23, 2023

But deep down, we know we could achieve better results with a different approach, after all in ML, there’s no one-size-fits-all solution. Cross-Validation: Perform cross-validation to ensure the models generalize well. It’s the one model that we’ve used time and time again, and it usually gets the job done.

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Mlearning.ai

NOVEMBER 30, 2023

The accuracy of these predictions typically surpasses that of a single decision tree, showcasing the strength of random forests in handling complex data sets in data science. This improvement often results in high accuracy, making GBMs a powerful tool in data science for solving complex problems.

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Heartbeat

AUGUST 29, 2023

Making the model learn more basic patterns in the data can help prevent overfitting. Cross-validation : Cross-validation is a method for assessing how well a model performs when applied to fresh data. Regularization : The approach of regularization penalizes the model for being overly complex.

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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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Mlearning.ai

FEBRUARY 27, 2023

Grid search utilizes cross validation too, so it is crucial to provide an appropriate splitting mechanism. Again, due to the nature of the problem we can’t just use plain k-fold cross validation. The parameter configuration that achieves the best result, will be the one to form the best estimator.

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Heartbeat

DECEMBER 19, 2023

Dataset Splitting from sklearn.model_selection import train_test_split # Split the dataset into features (X) and target (y) X = dataset[['User ID', 'Item ID']] y = dataset['Rating'] # Split the data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,

Deep Learning 52

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Heartbeat

JUNE 14, 2023

Cross Validated] Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. Advances in Neural Information Processing Systems 33 (2020): 15288–15299. [10] CVPR workshops.

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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. The only drawback of using a bigger model is computational cost. Bias vs.

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Heartbeat

MAY 29, 2023

The ML process is cyclical — find a workflow that matches. Check out our expert solutions for overcoming common ML team problems. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

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Mlearning.ai

MARCH 15, 2023

Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application. The scope of this article is quite big, we will exercise the core steps of data science, let's get started… Project Layout Here are the high-level steps for this project.

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phData

AUGUST 1, 2023

Dataiku is an industry-leading Data Science and Machine Learning platform that allows business and technical experts to work together in a shared environment. The platform accomplishes this by using a combination of no-code visual tools, for your code-averse analysts, and code-first options, for your seasoned ML practitioners.

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phData

AUGUST 1, 2023

Dataiku is an industry-leading Data Science and Machine Learning platform that allows business and technical experts to work together in a shared environment. The platform accomplishes this by using a combination of no-code visual tools, for your code-averse analysts, and code-first options, for your seasoned ML practitioners.

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