Pickl AI

DECEMBER 4, 2024

The rise of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML) , and Big Data analytics is reshaping industries and creating new opportunities for Data Scientists. Automated Machine Learning (AutoML) will democratize access to Data Science tools and techniques.

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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.

Artificial Intelligence 52

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AWS Machine Learning Blog

JUNE 22, 2023

Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering. Data preparation To extract data efficiently for training and testing, we utilize Amazon Athena and the AWS Glue Data Catalog.

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Pickl AI

NOVEMBER 5, 2024

According to a report by the International Data Corporation (IDC), global spending on AI systems is expected to reach $500 billion by 2027 , reflecting the increasing reliance on AI-driven solutions. Programming Skills Proficiency in programming languages like Python and R is essential for Data Science professionals.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

MAY 30, 2024

It combines elements of statistics, mathematics, computer science, and domain expertise to extract meaningful patterns from large volumes of data. Role of Data Scientists in Modern Industries Data Scientists drive innovation and competitiveness across industries in today’s fast-paced digital world.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

DECEMBER 12, 2024

Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. It identifies the optimal path for missing data during tree construction, ensuring the algorithm remains efficient and accurate. Lower values (e.g.,

Machine Learning 52

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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. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Data Transformation Transforming data prepares it for Machine Learning models. It’s simple but effective for many problems like predicting house prices.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

The MLOps Blog

DECEMBER 19, 2022

As Data Scientists, we all have worked on an ML classification model. Lesson 1: Mitigating data sparsity problems within ML classification algorithms What are the most popular algorithms used to solve a multi-class classification problem? Classification is one of the most widely applied areas in Machine Learning.

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