Data Science Dojo

AUGUST 28, 2023

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

Exploratory Data Analysis 195

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning 195

Dataconomy

JULY 28, 2023

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Towards AI

AUGUST 16, 2023

We can apply a data-centric approach by using AutoML or coding a custom test harness to evaluate many algorithms (say 20–30) on the dataset and then choose the top performers (perhaps top 3) for further study, being sure to give preference to simpler algorithms (Occam’s Razor).

AI 98

AI AI Machine Learning Machine Learning 98

Dataconomy

AUGUST 16, 2023

Data scientists are the master keyholders, unlocking this portal to reveal the mysteries within. They wield algorithms like ancient incantations, summoning patterns from the chaos and crafting narratives from raw numbers. Model development : Crafting magic from algorithms!

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

AWS Machine Learning Blog

JULY 31, 2023

Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building. These activities play a crucial role in extracting meaningful insights from raw data and improving model performance, leading to more robust and insightful results.

ML 98

ML ML Data Preparation Machine Learning 98

Pickl AI

MAY 30, 2024

You will collect and clean data from multiple sources, ensuring it is suitable for analysis. You will perform Exploratory Data Analysis to uncover patterns and insights hidden within the data. Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

JULY 12, 2024

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52