Pickl AI

NOVEMBER 4, 2024

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). These concepts help you analyse and interpret data effectively. You can create a new environment for your Data Science projects, ensuring that dependencies do not conflict.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

JULY 10, 2023

R’s data manipulation capabilities make cleaning and preprocessing data easy before further analysis. · Statistical Analysis: R has a rich ecosystem of packages for statistical analysis. These packages allow for text preprocessing, sentiment analysis, topic modeling, and document classification.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

AUGUST 27, 2024

This community-driven approach ensures that there are plenty of useful analytics libraries available, along with extensive documentation and support materials. For Data Analysts needing help, there are numerous resources available, including Stack Overflow, mailing lists, and user-contributed code.

Data Analysis 52

Data Analysis Data Analysis Python Data Analyst 52

Pickl AI

NOVEMBER 27, 2024

This workflow is useful when you can utilise Python’s numerical computation capabilities within an R-based analysis pipeline. Integration via Jupyter Notebooks Jupyter Notebooks offer a powerful environment for running Python in the same document, thanks to the support for multiple kernels.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data. Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

The MLOps Blog

MAY 31, 2023

As an example for catalogue data, it’s important to check if the set of mandatory fields like product title, primary image, nutritional values, etc. are present in the data. So, we need to build a verification layer that runs based on a set of rules to verify and validate data before preparing it for model training.

ML 59

ML ML Algorithm Machine Learning 59