Towards AI

AUGUST 16, 2023

We can define an AI Engineering Process or AI Process (AIP) which can be used to solve almost any AI problem [5][6][7][9]: Define the problem: This step includes the following tasks: defining the scope, value definition, timelines, governance, and resources associated with the deliverable.

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Pickl AI

JULY 12, 2024

This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes.

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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. Cross-Validation: Instead of using a single train-test split, cross-validation involves dividing the data into multiple folds and training the model on each fold.

Machine Learning 52

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Mlearning.ai

NOVEMBER 29, 2023

Preparation Stage Project goal definition — start with the comprehensive outline and understanding of minor and major milestones and goals. Data gathering and exploration — continuing with thorough preparation, specific data types to be analyzed and processed must be settled.

Machine Learning 52

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Mlearning.ai

JUNE 6, 2023

In this article, we will delve into the world of AutoML, exploring its definition, inner workings, and its potential to reshape the future of machine learning. It follows a comprehensive, step-by-step process: Data Preprocessing: AutoML tools simplify the data preparation stage by handling missing values, outliers, and data normalization.

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