Pinned Repositories
seahorse-workflow-executor
spark-hyperloglog
Interactive Audience Analytics with Spark and HyperLogLog
mr1azl's Repositories
mr1azl/auto-sklearn
mr1azl/bigquery-oreilly-book
Source code accompanying: BigQuery: The Definitive Guide by Lakshmanan & Tigani to be published by O'Reilly Media
mr1azl/calc_nbviewer
mr1azl/chrome-custom-tabs-android-example
Define custom color for chrome tabs(toolbar) and transition for chrome to meet your UI guideline when launching chrome browser from your app. For Tutorial http://androidcss.com/android/chrome-custom-tabs-android-example/
mr1azl/classifying-wine
Classify Wine with TensorFlow and Keras
mr1azl/Crime-Analysis-Prediction
Linear Model to predict the crime rate of North Carolina. Detailed EDA done prior to building model
mr1azl/custom-tabs-client
Chrome custom tabs examples
mr1azl/data-engineer-roadmap
Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups
mr1azl/Data-Science--Cheat-Sheet
Cheat Sheets
mr1azl/deep-conv-attr
An implementation of our CIKM 2018 paper "Deep Conversion Attribution with Dual-attention Recurrent Neural Network"
mr1azl/freshdatashapes
A 3-minute presentation on visualizing math
mr1azl/GrowthPrediction
Predict highest growth customer
mr1azl/High-Performance-Spark
mr1azl/Kafka-definitive-guide
mr1azl/mr1azl.github.io
mr1azl/Online-Courses-Learning
Contains the online course about Data Science, Machine Learning, Programming Language, Operating System, Mechanial Engineering, Mathematics and Robotics provided by Coursera, Udacity, Linkedin Learning, Udemy and edX.
mr1azl/pagerank
Page Rank for Evolving graphs using an incremental algorithm
mr1azl/python-kafka-benchmark
mr1azl/qa
Q&A - Give WordPress a fully-featured questions and answers section – just like StackOverflow, Yahoo Answers and Quora
mr1azl/refarch-analytics
This project provides a reference implementation for building and running analytics applications deployed on hybrid cloud environment
mr1azl/refarch-cognitive-analytics
Present a reference implementation for a business application linking cognitive and analytics to learn customer's behavior and assess customer risk to churn. It is based on structured data, machine learning algorithm, data movement, and cognitive services for classifying unstructured data.
mr1azl/Simple-Probabalistic-Model-For-Google-Analytics-Users
mr1azl/sklearn-porter
Transpile trained scikit-learn models to C, Java, JavaScript and others.
mr1azl/sp
mr1azl/spark-udf
mr1azl/spydra
Ephemeral Hadoop clusters using Google Compute Platform
mr1azl/training-data-analyst
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
mr1azl/tsdb-query-node
Tuned version of TSDB to be used with Splicer (co-located with HBase)
mr1azl/wiki
mr1azl/wine-classification
Using supervised learning classification techniques to predict wine samples