Pickl AI

FEBRUARY 4, 2024

The platform employs an intuitive visual language, Alteryx Designer, streamlining data preparation and analysis. With Alteryx Designer, users can effortlessly input, manipulate, and output data without delving into intricate coding, or with minimal code at most. What is Alteryx Designer?

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Pickl AI

JULY 12, 2024

Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes. This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models.

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. The type of data you collect is essential, and it falls into two main categories: structured and unstructured data. Decision trees are easy to interpret but prone to overfitting.

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Pickl AI

MAY 30, 2024

It systematically collects data from diverse sources such as databases, online repositories, sensors, and other digital platforms, ensuring a comprehensive dataset is available for subsequent analysis and insights extraction. Sources of Data Data can come from multiple sources. Removing outliers is also necessary.

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Mlearning.ai

NOVEMBER 29, 2023

Decision Trees ML-based decision trees are used to classify items (products) in the database. This is the applied machine learning algorithm that works with tabular and structured data. In its core, lie gradient-boosted decision trees. Obviously, this one is best for commercial analyses.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Data Collection: Sources and Types of Data Data comes in various forms , broadly categorised as structured and unstructured. databases, CSV files).

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The MLOps Blog

DECEMBER 19, 2022

Lesson 1: Mitigating data sparsity problems within ML classification algorithms What are the most popular algorithms used to solve a multi-class classification problem? Adding vectors to the index (xb are database vectors that are to be indexed). Creating the index. index.hnsw.efSearch = 16 # Setting the value for efSearch.

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