Data Science Dojo

JULY 5, 2024

The torchvision package includes datasets and transformations for testing and validating computer vision models. Scikit-learn Scikit-learn is a versatile Python library that offers various algorithms and model evaluation metrics, including cross-validation and grid search for hyperparameter tuning.

Machine Learning 322

Machine Learning Machine Learning ML ML 322

Mlearning.ai

JANUARY 27, 2023

We use some of the data for training and some for testing (we will not use test data for training). How we do this is the subject of the concept of cross-validation. I will develop a model using the training data (blue) and apply it to my test data (red). Diagram of k-fold cross-validation.

Cross Validation 52

Cross Validation Machine Learning Machine Learning Data Mining 52

Mlearning.ai

APRIL 10, 2023

No Problem: Using DBSCAN for Outlier Detection and Data Cleaning Photo by Mel Poole on Unsplash DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. DBSCAN works by partitioning the data into dense regions of points that are separated by less dense areas.

Algorithm 52

Algorithm Clustering Cross Validation Machine Learning 52

Pickl AI

MAY 24, 2023

Scikit-Learn Scikit Learn is associated with NumPy and SciPy and is one of the best libraries helpful for working with complex data. Its modified feature includes the cross-validation that allowing it to use more than one metric. NumPy NumPy is one of the most popular Python Libraries for Machine Learning in Python.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

JULY 12, 2024

Pandas: A powerful library for data manipulation and analysis, offering data structures and operations for manipulating numerical tables and time series data. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52