Applied machine learning coursera materials
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Updated
Feb 27, 2018 - Jupyter Notebook
Applied machine learning coursera materials
Machine Learning-based research for UPMC
Applied artificial intelligence is the implementation of AI tech to solve real world problems. It is a rapidly growing field that is set to shape our future with cutting edge technologies and perhaps even change the world as we know it today!
ISA_I 2024/2025 @ FIIT STU in Bratislava
Summer is the best season in the real estate market and housing prices vary based on a number of features. Data from 2 such Summers is taken about the Kings County Housing Prices. Two Jupyter Notebooks depicting the various Regression models is used to depict how the number of bedrooms, waterfront location, total area in square feet, etc. affect…
Applied Machine Learning in Python
Implementation of various of algorithms such as EM for Topic Modeling, High Dim. Classification, PCA, etc. described in "Applied Machine Learning" by David Forsyth
a structured 3-year Applied AI Bachelor degree path involves covering foundational courses, specialized AI topics, practical applications, and supplementary resources
Machine Learning and Deep Learning in Bioinformatics - Master's thesis repository
Work has been done on COVID-19 Bangladesh situation .Where Data Analysis, Data Visualization, Supervised Learning and Unsupervised Learning are used.
Conducted data analysis using statistical tools and complex visualizations; trained logistic regression, k-nn, kernelized svm, and random forest models; performed hyperparameter tuning and error analysis. Tech: Python (Seaborn, Matplotlib, Pandas, Scikit-Learn)
My Assignments @ Athens University of Economics and Business
This repo has all the files that I used during the studies of ML and DS
Xandly5 is a lyrics generator powered by Natural Language Processing using the Keras and TensorFlow frameworks.
maxent.blog: On Recommendation systems, information retrieval, platforms, software architecture, ML, NLP.
[French] TPs du cours d'initiation à la recherche opérationelle du M2 SSD de l'Univ. Grenoble Alpes
The aim of this practical course is to start from a simple deep learning model implemented in a notebook, and port it to a ‘reproducible’ world by including code versioning (Git), data versioning (DVC), experiment logging (Weight & Biases), hyper-parameter tuning, configuration (Hydra), and ‘Dockerization’.
Metis Data Science Bootcamp : Project Directory
Repository for IBM Machine Learning
Python3 implementation of gender detection from speech using GMM.
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