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flo7up / relataly-public-python-tutorials

Star 143

Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

python twitter-bot data-science machine-learning reinforcement-learning deep-learning time-series neural-network recurrent-neural-networks feature-selection openai neural-networks stock-price-prediction churn-prediction keras-tensorflow stock-market-prediction time-series-prediction time-series-forecasting scikit-learn-python openai-api
  • Updated Aug 9, 2023
  • Jupyter Notebook
Machine-Learning-with-Scikit-Learn-Python-3.x

reddyprasade / Machine-Learning-with-Scikit-Learn-Python-3.x

Star 52

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised lea…

python machine-learning reinforcement-learning vector scikit-learn sklearn machine-learning-algorithms scikit-image regression prediction consists supervised-learning classification semi-supervised-learning python-3 unsupervised-learning scikit-model scikit-learn-api scikit-learn-python
  • Updated Jun 16, 2021
  • Jupyter Notebook
Machine_Learning

Lawrence-Krukrubo / Machine_Learning

Star 7

Machine learning is the sub-field of Computer Science, that gives Computers the ability to learn without being explicitly programmed (Arthur samuel, American pioneer in the field of Computer gaming and AI , coined the term Machine Learning in 1959, while at IBM )

python tensorflow linear-regression supervised-learning logistic-regression clustering-algorithm knearest-neighbor-algorithm classification-algorithm unsupervised-machine-learning scikit-learn-python
  • Updated Dec 14, 2020
  • Jupyter Notebook

RegaipKURT / ScikitLearnWithFUN

Star 0

This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

python data-science machine-learning scikit-learn jupyter-notebook data-visualization data-analytics data-analysis machine-learning-pipelines scikitlearn-machine-learning machine-learning-models scikit-learn-python data-science-pipelines machine-learning-pipeline
  • Updated Jul 16, 2021
  • Jupyter Notebook

dpscience / DMLLTDetectorPulseDiscriminator

Star 3

DMLLTDetectorPulseDiscriminator - A supervised machine learning approach for shape-sensitive detector pulse discrimination in lifetime spectroscopy applications

machine-learning scikit-learn detector artificial-intelligence discriminator high-energy-physics materials-science time-of-flight scikitlearn-machine-learning lifescience fluorescence-microscopy-imaging positron-emission-tomography supervised-machine-learning pulse-detection gaussian-naive-bayes-implementation lifetime-spectroscopy positron-lifetime-spectra fluorescence-correlation scikit-learn-python pulse-shape-discrimination
  • Updated Jan 20, 2021
  • Python

UtsavMurarka / MXene-machine-learning

Star 5

Classification of MXenes into metals and non-metals based on physical properties

machine-learning deep-learning neural-network random-forest physics scikit-learn sklearn machine-learning-algorithms logistic-regression machinelearning atoms decision-trees molecules scikitlearn-machine-learning atomic-physics sklearn-classify knn-classifier sklearn-library machinelearning-python scikit-learn-python
  • Updated Oct 19, 2020
  • Jupyter Notebook

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