As DS application demo part of the "Daas (Data as a service) repo", this repo using jupyter notebook (mainly) as media showing step-by-step analysis and ML/DL approaches on various data science subjects. The idea is : demo how does a data scientist deal with a new dataset, pre-process the data, do exploration analysis (EDA), then running suitable model and offering suggestions with business feasibility and acceptable statistical errors. (i.e. DS workflow : business understanding -> data preprocess -> EDA -> data understanding -> analysis/modeling ). Main focus of this project: 1) Statistics/ML analysis 2) ML theory/algorithms explanation 3) Spark op/ML demo

• Daas (Data as a service) repo : Data infra build -> ETL build -> DS application demo
• Airflow Heroku demo : airflow-heroku-dev
• Mlflow Heroku demo : mlflow-heroku-dev

Quick_start.md

├── DE_course       : Code for Udacity data engineer course 
├── DL_             : Deep learning relative projects  
├── DS_algorithms   : Build Data science model from scratch 
├── GPU             : GPU relative code 
├── ML_             : Machine learning relative projects  
├── README.md
├── R_              : R programming language relative projects 
├── SPARK_          : Pyspark basics/op/ML/ETL notebook demo projects
├── Statistics_     : Statistics relative projects 
├── archived        : Archived code/projects 
├── doc             : Doc for quick start, theory paper, pic.. and so on
├── ml_demo.py 
├── notebook        : Jupyter notebook relative projects (nb server/magic..)
├── project         : Archived projects 
├── pytorch_        : Pytorch relative projects 
├── tensorflow_     : Tensorflow relative projects
└── utility         : Utility scripts for ML/DL model tuning, DS plots...

Machine Learning

• Gradian Decent - Main model optimization algorithms demo
• Linear Regression - Simplest regression model
• Logistics Regression - Simplest classification model
• Support vector machine -
• Decision Tree - Simple non-linear regression/classification model
• L1 L2 Regularization - Basic model tuning method

Tensorflow Demo

• TF Linear Regression - TF Linear Regression demo
• TF Random Forest - TF Random Forest Classification demo

Statistics

• Confidence Intervals - Go through the confidence interval calculation from distributions
• AB TEST Part 1 - Hypothesis Test | P-value | T-test
• AB TEST Part 2 - Bootstrapping
• TIME SERIES Part 1 - Stationary

Spark

spark op intro

• Pyspark Basic 1 - Basic spark ops (transform & action): RDD,Map,FlatMap, Reduce,filter, Distinct, Intersection
• Pyspark Basic 2 -Basic spark ops : load csv,dataframe,SparkSQL, transformation in [RDD, dataframe, SparkSQL]
• Pyspark Basic 3 -Basic spark ops : Spark DataFrame OP

spark ML intro

• Pyspark LinearRegression nb step by step demo - Spark Linear Regression model tutorial
• Pyspark LogisticRegression nb step by step demo - Spark Logistic Regression model tutorial
• Pyspark Pipeline/Index/Encode nb step by step demo - Train a spark ML model via pipeline and preprocess (index/encoder)
• Pyspark Tree models step by step demo - Train spark ML tree models (Decision tree/Random Forest/Gradient boosting)
• Pyspark Kmeans models step by step demo - Train spark ML cluster kmeans models
• Pyspark LinearRegression demo - Train a linear model with Spark ML framework
• Pyspark LinearRegression Grid Search demo - Train a Grid Search tuned linear model with Spark ML framework
• Pyspark Collaborative Filtering demo - Spark recommdation algorithm (ALS model) demo with movielens dataset

spark APP

• Pyspark Data Preprocess 1 : PTT - Digest PTT (批踢踢實業坊) data via Pyspark batch operations. PTT
• Pyspark Data Preprocess 2 : UBER Digest UBER data via Pyspark batch operations.
• Pyspark Streaming demo 1 - a simple word-count app data via Pyspark stream

• dev