Learn Machine Learning with machine learning tutorials for beginners, ml practicals, ml excerices, Machine Learning Projects, Interview Questions
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Updated
Nov 15, 2023
Learn Machine Learning with machine learning tutorials for beginners, ml practicals, ml excerices, Machine Learning Projects, Interview Questions
This project utilizes logistic regression to classify numbers 0 and 1 using sign language gestures. It successfully achieves the task of sign language classification, reaching a test accuracy of 93.54%.
A Python code for data analysis and salary predictions using a multiple linear regression model. The code calculates the intercept and coefficients of the model and makes predictions on sample data.
This Python code represents a machine learning project that builds a simple linear regression model using experience and salary data. It plots the data, constructs the regression model, and visualizes the results.
The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.
Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.
Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.
Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.
Workshop on Deep Learning for Health and Life Sciences
Supports de la conférence "Machine Learning pour tous avec python" présentée au Breizhcamp 2019
♂️♀️ Detect a person's gender from a voice file (90.7% +/- 1.3% accuracy).
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
Creating a logistic regression algorithm without using a library and making cancer classification with this algorithm model (Kaggle Explained)
In this tutorial we'll bring the TensorFlow 2 Quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.
Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
Code and files to go along with CS329s machine learning model deployment tutorial.
Algotrading101 article about Sklearn
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