Pickl AI

APRIL 13, 2025

Classification is a subset of supervised learning, where labelled data guides the algorithm to make predictions. Example: Predicting Customer Churn Logistic Regression is often used to predict whether a customer will churn based on historical data like usage patterns and billing information.

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Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., Taxonomy of the self-supervised learning Wang et al. 2022’s paper.

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IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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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!

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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!

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Pickl AI

JULY 17, 2023

Unsupervised Learning is a Machine Learning technique where the users do not require to supervise the model. Significantly, the technique allows the model to work independently by discovering its patterns and previously undetected information. Hence, it is considered as one of the best-unsupervised learning algorithms.

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Mlearning.ai

FEBRUARY 17, 2023

Basically, Machine learning is a part of the Artificial intelligence field, which is mainly defined as a technic that gives the possibility to predict the future based on a massive amount of past known or unknown data. ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning.

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Pickl AI

JULY 31, 2023

Understanding Eager Learning Eager Learning, also known as “Eager Supervised Learning,” is a widely used approach in Machine Learning. Examples of Lazy Learning Algorithms: K-Nearest Neighbors (k-NN) : k-NN is a classic Lazy Learning algorithm used for both classification and regression tasks.

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Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. spam email detection) and regression (e.g.,

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Mlearning.ai

NOVEMBER 29, 2023

The downside of overly time-consuming supervised learning, however, remains. Classic Methods of Time Series Forecasting Multi-Layer Perceptron (MLP) Univariate models can be used to model univariate time series prediction machine learning problems. In its core, lie gradient-boosted decision trees.

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Pickl AI

SEPTEMBER 12, 2024

By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success. Data Science is the art and science of extracting valuable information from data.

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PyImageSearch

SEPTEMBER 16, 2024

Machine Learning Methods Machine learning methods ( Figure 7 ) can be divided into supervised, unsupervised, and semi-supervised learning techniques. Figure 7: Machine learning methods for identifying outliers or anomalies (source : Turing ). unusual network traffic patterns).

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DagsHub

DECEMBER 23, 2024

We must understand that not all the data samples contribute to providing valuable information. Faster Learning Curve Active Learning achieves better model performance with fewer labeled examples by focusing on the most informative cases. But why is this an important and valuable approach?

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Pickl AI

DECEMBER 9, 2024

For example, a model may assume that similar inputs produce similar outputs in supervised learning. Inductive bias is crucial in ensuring that Machine Learning models can learn efficiently and make reliable predictions even with limited information by guiding how they make assumptions about the data.

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Google Research AI blog

NOVEMBER 28, 2022

Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year. Arik , Deniz Yuret, Alper T.

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