Pinned Repositories
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's Repositories
aligeekk/NLP-with-SVM-Random-Forest-NaiveBayes
A simple natural Language processing and classifier implemented using multiple classification methods
aligeekk/Udacity-Data-Visualization-ProsperLoan
Udacity Data Visualization Project
aligeekk/Feature-Selection-techniques
aligeekk/maths-for-ml
Notes and solutions for the Mathematics for Machine Learning Specialization
aligeekk/Detection-of-Advertisement-through-Machine-Learning-Approach
Classification of Advertisement URL using Machine Learning.
aligeekk/CyberSecurityWithML
Cybersecurity with Machine learning techniques
aligeekk/machinelearnjs
Machine Learning library for the web and Node.
aligeekk/aihub
I use this repository for my Youtube channel where I share videos about Artificial Intelligence. The repository includes Machine Learning, Deep Learning, and Reinforcement learning's code.
aligeekk/PhishingDetection-using-Logistic-Regression
Detects whether a given URL is malicious or safe one
aligeekk/nlp_course
YSDA course in Natural Language Processing
aligeekk/LOANPREDICTION-USING-DECISION-TREES
Attempted to predict the current status of loan to a given borrower by using Lending club lone data set using decision tree classifier (scikit-learn).The loan data records with title field of credit card, medical and debt are used. A loan status category of 0 is considered to be good because the loan status is either Fully Paid or Current. A loan status category of 1 is considered to be poor because the loan status is either Late(for any time period) or charged off .Thus we will train our model which will predict the class of the borrower so as to make it easy for the lender to decide the grant of loan
aligeekk/Feature-Selection-using-Reinforcement-Learning
Feature selection as reinforcement learning problem and performance comparison with traditional feature selection methods
aligeekk/MSFT-Virus-Detection
Microsoft's virus detection competition on Kaggle
aligeekk/ProsperLoan
Data Visualisation project for Udacity Data Analyst Nanodegree
aligeekk/Feature-Selection
Features selector based on the self selected-algorithm, loss function and validation method
aligeekk/URL-categorization-using-machine-learning
aligeekk/featselection
Feature selection methods for text classification
aligeekk/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.
aligeekk/Hands-On-Machine-Learning
Contains Jupyter Notebooks/Resources provided by the author and my work on problem sets.
aligeekk/pyqsar_tutorial
PyQSAR is Python package for feature selection
aligeekk/Text_Classification
Text Classification Algorithms: A Survey
aligeekk/Malicious-URL-Detection-using-Machine-Learning
A simple Logistic Regression Alogrithm to classify Malicious Url's
aligeekk/DataScienceResources
Open Source Data Science Resources.
aligeekk/AdversarialVirusDetection
Adversary - Counter-adversary game in malware detection application
aligeekk/100-Days-Of-ML-Code
100 Days of ML Coding
aligeekk/algorithms
Algorithms in python and C
aligeekk/DataStructuresAndAlgorithmsMadeEasy
Data Structures And Algorithms Made Easy
aligeekk/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
aligeekk/Machine-Learning---Intrest-Rate-Prediction-for-Peer-to-peer-Lending-Group
Machine Laerning - Intrest Rate Prediction for Peer to peer Lending Group
aligeekk/Machine-Learning-Based-Classification-of-Cervical-Cancer-Using-K-Nearest-Neighbor-Random-Forest-and
Cervical cancer is the second most common type of cancer that is found in the women worldwide. Generally, cancer caused due to irregular growth of cells in a particular area that or have the potential to spread to the other parts of the body as well. Identification of a cervical cancer test is an examination of the tissue taken from a particular region, which might contain cancerous cells through biopsy, is exceptionally challenging because these types of cells does not offer unusual color or texture variants from the standard cells. To identify the abnormalities in human cell the high-level digital image processing technologies are already present in the market which very costly concerning the money. Therefore, we are proposing the model which going to classify whether a female patient has cervical cancer or not. We are using various attributes from real-life and performing a feature selection algorithm Recursive Feature Elimination (RFE). Afterward, making classification models using three machine-learning algorithms like K-Nearest Neighbor (KNN), Random Forest and Multilayer Perceptron (MLP), MLP is a type of the Artificial Neural Network (ANN) algorithm whereas KNN and Random Forest is a supervised type of algorithm.