341 skills found · Page 5 of 12
mukul-mschauhan / Machine Learning ProjectsThis Repository comprises of Statistical Analytics and Machine Learning Projects
eugene123tw / ISLR NotesNotes included mathematical proofs related to Machine Learning or Statistical Learning.
danielkelshaw / ConcreteDropoutPyTorch implementation of 'Concrete Dropout'
Azure-Samples / LearnAnalytics MicrosoftMLIntroduction to Statistical Machine Learning with MicrosoftML
Open-Sourced-Olaf / Code KindleSource code to pseudocode generator using Statistical machine learning techniques for C/C++ and python3.
YangLabHKUST / MATH 4432 Statistical Machine LearningTutorials for MATH 4432 Statistical Machine Learning, 2025 Fall
aya49 / Fingerprint LivenessCompares several different machine learning models and statistical features to predict the liveness of the fingerprint biometric.
Techtonique / NnetsauceStatistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks (currently Python & R)
h-sami-ullah / Deep Learning For Time Series ForcastingDesigning a Machine Learning algorithm to predict stock prices is a subject of interest for economists and machine learning practitioners. Financial modelling is a challenging task, not only from an analytical perspective but also from a psychological perspective. After 2008 financial crisis, many financial companies and investors shifted their interest towards predicting future trends. Most of the existing methods for stock price forecasting are modelled using non-linear methods and evaluated on specific data sets. These models are not able to generalize for diverse datasets. Financial time series data is highly dynamic in nature and makes it difficult to analyze through statistical methods. Recurrent Neural Networks (RNN) based Long Short- Term Memory (LSTM) networks were able to capture the patterns of the sequences data meanwhile statistical methods tried to generalize by memorizing data instead of recognizing patterns. In this work, we examined the performance of LSTM model and statistical models over stock prices of different companies to generalize the model. The experimental results of this study show that, LSTM network outperformed traditional statistical methods like ARIMA, MA and AR models. Furthermore, we have noticed that, LSTM network was able to perform consistently on different data sets while statistical methods showed varied performance. Through this project, we addressed the gaps in current models of stock price prediction in both economic and machine learning perspective.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
ginevracoal / Statistical Machine LearningProbabilistic Machine Learning course lab @UNITS
yemregundogmus / A Glimpse To Turkish Political Climate With Statistical Machine LearningNo description available
Cumulocity-IoT / R PmmlGenerate PMML for various machine learning and statistical models.
brandonckelly / Bck StatsRoutines for implementing various statistical and machine learning techniques.
PrabhkiratSingh123 / Wine Quality Prediction MLMachine learning–based wine quality classification using ensemble models, featuring White vs Red wine comparison, feature importance analysis, and statistical validation.
kartoun / EmrbotsEMRBots are experimental artificially generated electronic medical records (EMRs). The aim of EMRBots is to allow non-commercial entities (such as universities) to use the artificial patient repositories to practice statistical and machine-learning algorithms. Commercial entities can also use the repositories for any purpose, as long as they do not create software products using the repositories.
Lovely9899 / Student Performance Analysis And PredictionStudent Performance Analysis and Prediction is a data-driven approach to evaluating and forecasting academic outcomes based on various factors such as demographics, attendance, past academic records, socio-economic background, and behavioral patterns. By leveraging techniques such as statistical analysis, machine learning, and data visualization.
NBISweden / Workshop MlbiostatisticsGeared towards life scientists wanting to be able to understand and use basic statistical and machine learning methods
amitpaul2004 / Data AnalyticsA collection of Data Science and Machine Learning projects featuring real-world datasets, statistical analysis, and advanced visualizations. Demonstrates skills in Python, data analysis, model building, and visualization beyond basics.
toncho11 / ML ExamplesA list of examples for Machine Learning: Transformers, Large Language Models, Keras for Deep Learning, Transfer Learning with PyTorch for Image Classification, EEG Classification, Decision Trees, Statistical Hypothesis Testing, ABTesting any many more.
