Alfred's repositories
Customer-Journey-Analytics
Analytics for building Customer Journey Map in Ecommerce
advanced-principle-component-analysis
Principle component analysis with varimax rotation and various dimensionality reduction methods based on scikit-learn PCA class
Customer-Segmentation
Customer Segmention in Ecommerce
data-modeling-nosql
Data modeling with Apache Cassandra
data-modeling-with-postregs
ETL pipeline to build postres database for music store
data-structures
Data Scientist and Software Engineer technical interview questions related to data structures .
deploying-machine-learning-models
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
disaster-response-workflow-tool
Web app classifying and prioritizing disaster response messages
Flowers-Image-Recognition
Neural network classifying flowers
Target-Marketing-Donors-Classification
Estimating gross income from demographic data
imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Job-Post-Parser
Create word cloud of responsibilities, requirements and tools from data science job post from glassdoor
mathjax-corrector
Recursively parse root directory tree and correct broken MathJax expressions in html files.
page-spider
Learning Python with PyCharm
Recommendation-systems
Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
unscramble-computer-science-problems
Time Complexity Analysis