aligeekk

alienvault-cyber-attack-prediction

Uses ML to try and predict cyber attacks using AlienVault OTX threat intel.

Basic-Mathematics-for-Machine-Learning

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

cyber-matrix-ai

Collection of cyber security and "AI" relevant topics

CyberSecurity-Anomaly_Detection

I am working on generating a ML pipeline using Spark for anomoly detection from unstructured data from parsed system logs.

FCM_FNN

Implementation of Fuzzy Cognitive Maps Based on Fuzzy Neural Network

FNNP

Code released for "FNNP: Fast Neural Network Pruning Using Adaptive Batch Normalization"

homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

keras-malicious-url-detector

Malicious URL detector using keras recurrent networks and scikit-learn classifiers

Machine-Learning-with-Python

Python code for common Machine Learning Algorithms

Math-of-Machine-Learning-Course-by-Siraj

Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

aligeekk/Basic-Mathematics-for-Machine-Learning

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

aligeekk/Math-of-Machine-Learning-Course-by-Siraj

Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

aligeekk/practicalAI

📚 A practical approach to machine learning to enable everyone to learn, explore and build.

aligeekk/Stats-Maths-with-Python

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

aligeekk/Tutorials

Ipython notebooks for math and finance tutorials

aligeekk/ann_fsl

Feature selection with deep neural networks

aligeekk/best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

aligeekk/coursera

Data sets and scripts for Coursera Big Data Specialization.

aligeekk/deep-learning-illustrated

Deep Learning Illustrated (2019)

aligeekk/Email-Fraud-NLP-

A simple 0️⃣1️⃣ classifier employing various 💬 NLP and text processing 💬 techniques to flag fraudulent 📧

aligeekk/Ensemble-classifier

Deep neural network classifier that uses auto-encoders for feature selection. The primary objective is to evaluate the performance difference between regular classification models with topic modeling and deep neural network with auto-encoders. Tools/technologies used: Scikit-learn, Python machine learning models, Keras

aligeekk/Feature-Engineering-Feature-Selection-Techniques-

Coding an End-To-End Demonstration [Feature Selection Techniques]

aligeekk/Feature-Selection-for-Machine-Learning

Methods with examples for Feature Selection during Pre-processing in Machine Learning.

aligeekk/Feature-Selection-K-Means-and-Expected-Maximization-for-SGEMM-and-Australian-Weather-Dataset

To implement Feature selection, Dimensionality Reduction and use these features in Principal Component Analysis, Independent Component Analysis and Random Component Analysis for K Means, Expected Maximization and Neural network.

aligeekk/FeatureSelection

Embedded L1 Numerical FS with One Hot Encoding

aligeekk/gpt-3

GPT-3: Language Models are Few-Shot Learners

aligeekk/Hands-On-Reinforcement-Learning-With-Python-1

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

aligeekk/interpretable_machine_learning_with_python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

aligeekk/java-string-similarity

Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity ...

aligeekk/machine-learning-online-2018

ML Online Course Repository. Course videos on online.codingblocks.com

aligeekk/machinelearning

clustering, regression, classification, deep learning, neural networks, exploratory data analysis, feature selection

aligeekk/MaliciousURLDetection

Using machine learning algorithms to detect and identify malicious URLs

aligeekk/mealpy

A collection of the state-of-the-art MEta-heuristics ALgorithms in PYthon (mealpy)

aligeekk/Our-Datasets

aligeekk/python-bigdata

Data science and Big Data with Python

aligeekk/Python-implementation-of-Gray-Wolf-Optimizer-and-Improved-Gray-Wolf-Optimizer

Implementation of GWO and i-GWO with Python 3.9

aligeekk/python-string-similarity

A library implementing different string similarity and distance measures using Python.

aligeekk/wordcloud

aligeekk/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)

aligeekk/XuniVerse

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