12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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Updated
Jun 4, 2024 - HTML
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Compilation of R and Python programming codes on the Data Professor YouTube channel.
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.
🔉 👦 👧Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
The practitioner's forecasting library
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…
A fast, robust library to check for offensive language in strings, dropdown replacement of "profanity-check".
🔉 👦 👧 👩 👨 Speaker identification using voice MFCCs and GMM
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 )
Recognition of the images with artificial intelligence includes train and tests based on Python.
Efficient sparse matrix implementation for various "Principal Component Analysis"
This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.
Explore machine learning models and optimization techniques.
Codes for "Parkinson’s Disease Diagnosis: Effect of Autoencoders to Extract Features from Vocal Characteristics"
Analyzing the most affecting factors that deteriorate the health of a person and predicting the risk of developing dreadful diseases using Machine Learning.
DMLLTDetectorPulseDiscriminator - A supervised machine learning approach for shape-sensitive detector pulse discrimination in lifetime spectroscopy applications
poverty prediction and analysis
Decision tree implementation using python scikit-learn
Classification of MXenes into metals and non-metals based on physical properties
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