Hier geht es zur Version auf deutsch / link to the German version

You receive the knowledge from real projects and top notch technologies for, e.g., Big Data (e.g. with Apache Spark, PySpark, databricks), Security Incident and Event Management (e.g. with Splunk), Machine Learning and Data Science with Python.

Contact training(at)buhlmeier.com for details and competitive pricing.

Courses can be online or on location and are in German or English, slides are mostly in English

Machine Learning with Python – from basics to Large Language Models (LLM)

Machine Learning context
Machine Learning basics
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning for pattern recognition: Video and Tensorflow.js
Network architecture
Deeper into Machine Learning
Some Math
Reinforcement Learning
Exercise (Taxi Game Q-Learner)

Machine Learning mit Tensorflow
Backpropagation Algorithmn in more detail
Coding with tensorflow
Monte Carlo simulation
Neural Network with TF
Keras Tensorboard

Data processing steps, from source to prediction
Convolutional Layers in Tensorflow and their application
Time Series predictions with deep learning (Long – short term memory NN)
Text classification: IMDB sentiment analysis
Generative models
For image creation (exercise with own picture)
For text creation

Refinements and success factors
Summary and Outlook

Big Data Analysis with Apache Spark, PySpark, databricks, Python, Scala

Overview Apache Spark/PySpark and databricks
Install Apache Spark, PyCharm or VSCode
First exercises
Pandas Dataframes vs. PySpark Dataframes, Best practices
Exercises Dataframes
Spark Streaming
Spark ML Lib 
PySpark and SQL
Graph Theory
Scala Overview, Motivation
Installation and tests
Scala simple Exercises
Object Oriented Programminng with Scala
GraphX (Context, History, Restrictions)
Elastic Search Integration

Operations Research with Python

Definition, Problems and Solutions
Linear Models, Algebra and their Geometry with Exercise
Model Overview
MIP Library: (Mixed) Integer Programmierung with exercises (Knapsack, Scheduling)
More on the MIP Library: (Mixed) Integer Programmierung

Network Models, Network-Algorithmns, dynamic Models
Sensitivity analysis
Dynamic Programming
Neural Networks
Non-linear Programming
Queueing theory
Deterministic logistic
Probabilistic logistic
Python packages comparison
Conclusion and outlook

Optional advanced topics:
non-linear target function
advanced non-linear programming

SIEM and other applications with Splunk

Introduction and expectations
The context of Splunk
Splunk components
Splunk’s GUI basics
Search basics
Search using fields
The search language in more detail

Questions & Feedback
Create reports
Scheduled reports and alerts
The common information model
Installation assessment (queries that help to check your setup)
Splunk Apps/Add ons, e.g.:
Enterprise Security
Machine Learning Toolkit
Implement your requirements

Splunk Dashboard Studio and Dashboard Classic(see video in German)

Current Frameworks
Recap XML Dashboards
Classic vs. Dashboard Studio
Using Dasboard Studio
Chain Searches, Maps, ..
What are Tokens used for
Token Examples with XML Dashboards and Dasboard Studio


Database data ingestion and visualization with Splunk (see teaser on you tube in German)


Using DB Connect
Creating Identities
Creating Connections
Creating Database Outputs/Inputs
Creating and managing Database Lookups

Further Functions and Troubleshooting
Using SQL Explorer to make Live Reports
Executing SQL statements and stored procedures with the dbxquery Command
Monitoring Splunk DB Connect Health

Splunk Live Demonstration

Dr. Bühlmeier Consulting, Frankfurt am Main, Germany