Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 12, 2024

Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 8, 2024

SVMs can classify text documents with high accuracy and efficiency by transforming text data into numerical features using techniques like TF-IDF (Term Frequency-Inverse Document Frequency). Cross-validation is a valuable technique for assessing the model’s performance across different subsets of the data.

Machine Learning 52

Machine Learning Machine Learning Algorithm Support Vector Machines 52

Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

The MLOps Blog

APRIL 20, 2023

Annotation and labeling: accurate annotations and labels are essential for supervised learning. It’s easy to work with, supports asynchronous programming, and offers built-in validation and documentation features. These tools offer a user-friendly interface and support various annotation formats that you can export.

ML 64

ML ML Data Quality Cross Validation 64