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streamingphish

Python-based utility that uses supervised machine learning to detect phishing domains from the Certificate Transparency log network.

structure

Several languages of data structure practice

text-classification-using-lda-and-gcn

Constructed a structured heterogeneous text corpus graph to transform text classification problem into a node classification problem. Created semantic rich features by using Text GCN and topic modeling based approach-LDA which are then fed into a novel classification model.

text_classification

Text Classification Algorithms: A Survey

the-elements-of-reinforcement-learning

Reinforcement Learning (RL) is believe to be a more general approach towards Artificial Intelligence (AI). RL is the foundation for many recent AI applications, e.g., Automated Driving, Automated Trading, Robotics, Gaming, Dynamic Decision, etc. With concrete examples, this repository tries introduce clearly the basic elements of Reinforcement Learning, e.g., Agent, Environment, State, State Transition, Policy, Action, Reward, Future Return, Discounted Future Return, Exploration & Exploitation, Markov Decision Processing, The Bellman Equation, Policy-based Learning, Value-based Learning, etc.

transfer-learning-classifier

A command line application to take in data (in the case of the exposition, images of flowers) and construct a neural network model using transfer learning, e.g. with VGG11, to take advantage of feature selection capabilities of state-of-the-art image classifiers.

tutorials

Ipython notebooks for math and finance tutorials

udacity-data-visualization-prosperloan

Udacity Data Visualization Project

url-categorization-using-machine-learning

url-classification

Machine learning to classify Malicious (Spam)/Benign URL's

url-classifier

2018 BEng Honours Project to classify unseen phishing URLs

url-feature-extractor

Extracting features from URLs to build a data set for machine learning. The purpose is to find a machine learning model to predict phishing URLs, which are targeted to the Brazilian population.

urlnet

URLNet

virus_detection_sra

watts-strogatz

Python implementation of the Watts-Strogatz model for generating small-world networks

word_vector_example

This notebook will explain how to train your own word vector with raw text data.

word_vectors_game_of_thrones-live

This is the code for the "How to Make Word Vectors from Game of Thrones (LIVE) " Siraj Raval on Youtube

wordcloud

wrapper-method-forward-and-backward-selection

What is Feature selection? As the name suggests, it is a process of selecting the most significant and relevant features from a vast set of features in the given dataset. For a dataset with d input features, the feature selection process results in k features such that k < d, where k is the smallest set of significant and relevant features. So feature selection helps in finding the smallest set of features which results in Training a machine learning algorithm faster. Reducing the complexity of a model and making it easier to interpret. Building a sensible model with better prediction power. Reducing overfitting by selecting the right set of features. Feature selection methods For a dataset with d features, if we apply hit and trial method with all possible combinations of features then total 2^d — 1 models need to be evaluated for a significant set of features. It is a time-consuming approach, therefore, we use feature selection techniques to find out the smallest set of features more efficiently. There are three types of feature selection techniques : Filter methods Wrapper methods Embedded methods Difference between Filter, Wrapper and Embedded methods Filter vs. Wrapper vs. Embedded methods In this post, we will only discuss feature selection using Wrapper methods in Python. Wrapper methods In wrapper methods, the feature selection process is based on a specific machine learning algorithm that we are trying to fit on a given dataset. It follows a greedy search approach by evaluating all the possible combinations of features against the evaluation criterion. The evaluation criterion is simply the performance measure which depends on the type of problem, for eg. for regression evaluation criterion can be p-values, R-squared, Adjusted R-squared, similarly for classification the evaluation criterion can be accuracy, precision, recall, f1-score, etc. Finally, it selects the combination of features that gives the optimal results for the specified machine learning algorithm. Flow chart — Wrapper methods Most commonly used techniques under wrapper methods are: Forward selection Backward elimination Bi-directional elimination(Stepwise Selection)

xuniverse

xverse (XuniVerse) is collection of transformers for feature engineering and feature selection

zero_to_deep_learning_video

Repository for the Zero to Deep Learning® Video Course