Data Science Dojo

AUGUST 19, 2024

.” Unsupervised learning: In this type of learning, the model is trained on unlabeled data, and it must discover patterns or structures within the data itself. This is used for tasks like clustering, dimensionality reduction, and anomaly detection. Python Explain the steps involved in training a decision tree.

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. regression, classification, clustering).

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Pickl AI

APRIL 3, 2023

Significantly, Supervised Learning is practical in two types of tasks- Classification: the goal is to predict a categorical label for each input data point Regression: the goal is to predict a continuous value. Significantly, there are two types of Unsupervised Learning: Clustering: which involves grouping similar data points together.

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Mlearning.ai

APRIL 12, 2023

Using different machine learning algorithms for performance optimization: Several machine learning algorithms can be used for performance optimization, including regression, clustering, and decision trees. Clustering algorithms can be used to group users based on behavior patterns and optimize performance for each group.

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ML @ CMU

MAY 12, 2023

If local training minimizes the effect of data heterogeneity but enjoys no DP noise reduction, and contrarily for FedAvg, it is natural to wonder whether there are personalization methods that lie in between and achieve better utility. This is certainly not perfect as it ignores population-level modeling (e.g.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Unsupervised Learning Unsupervised learning involves training models on data without labels, where the system tries to find hidden patterns or structures.

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DagsHub

APRIL 21, 2024

Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques. It also provides tools for model evaluation , including cross-validation, hyperparameter tuning, and metrics such as accuracy, precision, recall, and F1-score.

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