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Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

machine-learning ocr neural-network linear-regression octave pca logistic-regression feedforward-neural-network support-vector-machine backpropagation learning-curve anomaly-detection error-analysis

Updated Jan 7, 2018
MATLAB

nickcafferry / Machine-Learning-in-Molecular-Sciences

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2017 Summer School on the Machine Learning in the Molecular Sciences. This project aims to help you understand some basic machine learning models including neural network optimization plan, random forest, parameter learning, incremental learning paradigm, clustering and decision tree, etc. based on kernel regression and dimensionality reduction,…

machine-learning random-forest clustering feature-selection dimensionality-reduction artificial-neural-networks gaussian-processes learning-curve kernel-ridge-regression delta-learning-rule parameter-learning

Updated May 16, 2022
Jupyter Notebook

deeptechlabs / tuner.algo.extrapol

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Tuner Implementation of Extrapolating Learning Curves

hyperparameter-optimization orion learning-curve extrapolating-learning-curves frank-hutter

Updated Jun 24, 2018
Python

auriml / model_evaluation_exercise

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Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

cross-validation performance-metrics learning-curve model-complexity

Updated Mar 14, 2017
HTML

vaibhavnaagar / machineLearning-exercises

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Machine Learning Exercises from Online Course (Coursera)

machine-learning linear-regression pca classification logistic-regression gradient polynomial-regression spam-classification learning-curve feedforward fmincg

Updated Jan 11, 2017
MATLAB

konkyrkos / sentiment-classifier-classic-algorithms

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Tweet Sentiment Classifier using Classic Machine Learning Algorithms. This repository provides my solution for the 2nd Assignment for the course of Text Analytics for the MSc in Data Science at Athens University of Economics and Business.

python statistics random-forest metrics sklearn sgd recall logistic-regression learning-curve sentiment-classifier multinomial-naive-bayes probabilistic-classifiers statistical-significance-test dummyclassfier classifier-metrics-precision f1-scores

Updated Aug 19, 2020
Jupyter Notebook

farrellwahyudi / Predicting-Ad-Clicks-Classification-by-Using-Machine-Learning

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In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.