Python Machine Learning Client for SAP HANA (hana-ml) enable data scientists to use their own Python environments to easily leverage machine learning algorithms in SAP HANA Database Predictive Analysis Library (PAL) with its data and in-memory advantages.

In this blog post, a list of links of learning resources is shown as below:

1. blog posts written by data scientists(Dr. Raymond Yao, Dr. Likun Hou, Dr. Xin Chen) from SAP HANA  PAL to give a deep-dive on different topics.
2. a series of share and learn lectures for SAP HANA Machine Learning in Chinese organized by SAP Labs China Machine Learning Community.

Blog Posts

Outlier and Anomaly Detection

A series of blog posts with different techniques for outlier and anomaly detection is shown below.

Time Series Analysis

Time series analysis is a very powerful analytical tool and a crucial part of machine learning. With hana-ml, various time series analysis algorithms are introduced from the concept to practice.

Others:

Share Sessions

These series of share sessions contains the slides (in English) and recordings(in Chinese).

Topic 1: Outlier Detection by Statistical Tests

Topic 2: Outlier Detection by Clustering

Topic 3: Anomaly Detection by Classification

Topic 4: Basic Time Series Forecasting (ARIMA and Exponential Smoothing)

Thank you!

We will keep updating the list of share and learning resources. If you prefer any specific topic, please leave a comment under this post.

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