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Announcing the Winners of ‘The NFL Fantasy Football’ Data Challenge

Ocean Protocol

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

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The AI Process

Towards AI

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. 85% or more of AI projects fail [1][2].

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Feature Engineering in Machine Learning

Pickl AI

The growing application of Machine Learning also draws interest towards its subsets that add power to ML models. Key takeaways Feature engineering transforms raw data for ML, enhancing model performance and significance. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models.

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New Data Challenge: Aviation Weather Forecasting Using METAR Data

Ocean Protocol

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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The Easiest Way to Determine Which Scikit-Learn Model Is Perfect for Your Data

Mlearning.ai

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. You may need to import more libraries for EDA, preprocessing, and so on depending on the dataset you’re dealing with. Cross-Validation: Perform cross-validation to ensure the models generalize well.

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Sales Prediction| Using Time Series| End-to-End Understanding| Part -2

Towards AI

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

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Meet the winners of the Kelp Wanted challenge

DrivenData Labs

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?