Python code for common Machine Learning Algorithms
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Updated
Mar 10, 2024 - Jupyter Notebook
Python code for common Machine Learning Algorithms
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
General Assembly's 2015 Data Science course in Washington, DC
🍊 📊 💡 Orange: Interactive data analysis
For extensive instructor led learning
Text Classification Algorithms: A Survey
A python library for decision tree visualization and model interpretation.
A curated list of Best Artificial Intelligence Resources
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
2022 Coursera Machine Learning Specialization Optional Labs and Programming Assignments
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
Final project of IBM's course https://www.coursera.org/learn/machine-learning-with-python on coursera
An Interactive Approach to Understanding Deep Learning with Keras
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