Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Towards AI

JULY 3, 2024

Community & Support: Verify the availability of documentation and the level of community support. Some methods need a lot of resources therefore they might not be practical for huge datasets or real-time applications without a lot of computing power.

K-nearest Neighbors 105

K-nearest Neighbors Machine Learning Machine Learning Algorithm 105

Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. For example, in decision tree algorithms, entropy helps identify the most effective splits in data.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 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

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. Inductive Learning: A type of learning where a model generalises from specific examples to broader rules or patterns.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

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

MAY 31, 2023

Accordingly, it is possible for the Python users to ask for help from Stack Overflow, mailing lists and user-contributed code and documentation. Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow.

Data Science 52

Data Science Python Data Scientist Machine Learning 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

DagsHub

DECEMBER 23, 2024

Optimized Expert Time Active Learning ensures expert time is spent on cases where their expertise adds the most value. Suitable Applications Here are some of the suitable applications for pool-based active learning. Relies on explicit decision boundaries or feature representations for sample selection.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52

ML @ CMU

DECEMBER 2, 2024

Existing approaches also tie the tightness of bounds to the number of independent documents in the training set, ignoring the larger number of dependent tokens, which could offer better bounds. This work uses properties of martingales to derive generalization bounds that leverage the vast number of tokens in LLM training sets.

Deep Learning 227

Deep Learning Deep Learning Machine Learning Machine Learning 227