IBM Journey to AI blog

DECEMBER 20, 2023

Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms. Random forest algorithms —predict a value or category by combining the results from a number of decision trees.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Text feature extraction Objective: Transforming textual data into numerical representations.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

JULY 12, 2024

Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents. 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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 10, 2023

These packages allow for text preprocessing, sentiment analysis, topic modeling, and document classification. It allows data scientists to combine code, documentation, and visualizations in a single document, making it easier to share and reproduce analyses.

Data Science 52

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Pickl AI

SEPTEMBER 12, 2024

Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis. Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

DagsHub

SEPTEMBER 16, 2024

Additionally, specialized visualizations for specific domains, such as network graphs for social media analysis or geographical maps for spatial data, can be valuable. Good documentation ensures that users can easily understand the tool's functionalities and learn how to use them effectively.

Machine Learning 52

Machine Learning Machine Learning ML ML 52