Machine Learning

Data Science and Artificial Intelligence

Today, the topics data science, artificial intelligence, and machine learning constitute the backbone of any IoT or digitization solution, as they are actually responsible for value creation. This training provides an insight as to with which methods and technologies digital data can be processed, analyzed and used for a self-learning business optimization. The course is designed according to the requirements of the industry and comes with many interactive exercises, which have been worked out for both the direct user and the decision-taker. This permits a profound and comprehensive introduction into the areas of artificial intelligence and machine learning. For this purpose, the training imparts the required core competence for the generation or evaluation of existing concepts and creates the relation to practical application.

Course Contents

Introduction to Data Science, Machine Learning, and Artificial Intelligence
Basics of Python Regarding Data Processing, Statistics, and Data Virtualization
Machine Learning Workflow
Learning Scenarios and their Fields of Application (e.g. Predictive Analytics, Bots, Recommendation Services)
Machine Learning Methods in Comparison (from Decision Tree up to Deep Learning)
Artificial Neuronal Networks
Decision Trees
Support Vector Machines
Clustering
Evaluating and Validating Models Correctly
Overview of Software and Tools
Application Scenarios (What is a hype and where is potential?)
Questions and Fears in a Social Context
Consistent Interactive Hands-On Exercises

You will receive the comprehensive course documentation of the ExperTeach Networking series in German language. Optionally, we provide the printed version or an ExperTeach e-book.

Target Group

The course is offered for IT-oriented employees (from user to decision-taker) looking for an introduction to the topics data science, artificial intelligence, and machine learning, meant for practical application.

Knowledge Prerequisites

The students should have a basic understanding of and interest in the trend topics digitalization, Big Data, and IoT. Basic programming know-how would be advantageous.

1 Machine Learning at a Glance

1.1 What is Machine Learning?

1.1.1 Statistics again

1.1.2 The time is ripe.

1.1.3 Artificial Intelligence

1.2 Current examples

1.2.1 AlphaGo

1.2.2 Speech recognition

1.2.3 Online shopping

2 Machine Learning from A to Z

2.1 Intro and motivation

2.2 Basics by example

2.2.1 Preparation of the data

2.2.2 Feature extraction

2.2.3 Feature Engineering

2.2.4 Training

2.2.5 Performance

2.2.6 Optimization

2.2.7 Validation

2.3 Summary

3 Descriptive Statistics

3.1 Intro and motivation

3.1.1 Survivorship Bias and the Excel Question

3.2 Basics and Hands-On

3.3 Python - the basics

3.3.1 Scientific Computing in Python with numpy and pandas

3.3.2 Data visualization in Python with matplotlib

3.4 Basics of Statistics

3.4.1 Distributions

3.4.2 Central values

3.4.3 Class formation

3.4.4 Correlations

3.5 Summary

4 Adjustments and Minimizations

4.1 Intro and motivation

4.1.1 Common distributions

4.1.2 Normal distributions and fits

4.1.3 Expectations and experiences

4.2 Fit methods

4.2.1 The method of least squares

4.2.2 Likelihood Fits

4.3 Evaluating fits

4.3.1 Goodness of Fit

4.3.2 Many dimensions

4.3.3 Bias-Variance-Dilemma and Overfitting

4.3.4 Interpret fit results correctly

4.4 Matter-antimatter asymmetry

4.5 Summary

5 Learning scenarios and task definition

5.1 Intro and motivation

5.2 Learning scenarios

5.2.1 Supervised Learning

5.2.2 Unsupervised Learning

5.2.3 Reinforcement Learning

5.3 Task definition

5.4 What else is there?

5.5 Summary

6 Preparing the Data

6.1 Intro and motivation

6.2 Basics: features and characteristics

6.3 Cleaning the data

6.3.1 Outliers

6.3.2 Expert knowledge

6.4 Feature engineering

6.4.1 Encoding

6.4.2 Rounding, discretization

6.5 Feature Selection

6.5.1 Correlations

6.5.2 Reduction

6.6 Summary

7 Of Trees and Meshes

7.1 Intro and Motivation

7.2 Differentiation between trees and nets

7.2.1 Decision Tree

7.2.2 Random Forest

7.2.3 Perceptron

7.2.4 Multi Layer Perceptron

7.2.5 Differentiation from Deep Learning

7.3 Classification of Marbles

7.3.1 Manual separation

7.3.2 Structure of the data pipeline

7.3.3 K-Means

7.3.4 Random Forest

7.3.5 Multi-layer perceptron

7.3.6 Features

7.4 Summary

8 Validation and Performance

8.1 Intro and Motivation

8.2 Performance analysis

8.2.1 Prediction probabilities

8.2.2 False positives - fall-out

8.2.3 Sensitivity also matters.

8.2.4 Confusion Matrix

8.2.5 Derived ratios and rates

8.2.6 ROC and AUC

8.3 Validation examples

8.3.1 Train-Test-Split

8.3.2 K-folds

8.3.3 Cross validation

8.4 Summary

9 Applications and practical examples

9.1 Chatbots

9.1.1 What is a chatbot?

9.1.2 Other bot types

9.1.3 Turing test

9.1.4 How they work

9.1.5 The most popular chatbots

9.2 Recommendation services

9.2.1 What is a recommender system?

9.2.2 Influencing variables

9.2.3 Examples

9.2.4 The Netflix price

9.3 Image recognition

9.3.1 Image recognition for autonomous driving.

9.3.2 Medical image recognition

9.3.3 Outlook

9.4 Industrial application and challenge

9.4.1 Predictive maintenance

9.4.2 Predictive Quality

9.5 Natural Language Processing

9.5.1 Character Recognition

9.5.2 Syntactic tasks

9.5.3 Semantic tasks

9.5.4 Further fields of application

9.5.5 Techniques using the example of sentiment analysis

10 ML methods in comparison

10.1 Intro and Motivation

10.2 New ML Models

10.2.1 Support Vector Machines

10.2.2 K-Nearest Neighbors

10.2.3 Clustering Methods

10.3 Test Data Sets

10.3.1 Decision Tree

10.3.2 Multi Layer Perceptron (MLP)

10.3.3 Deep MLP

10.3.4 K-Nearest Neighbors

10.3.5 Support Vector Machines

10.3.6 K-Means

10.3.7 Gaussian Mixture

10.3.8 DBSCAN

10.4 Summary

11 Software and Tools

11.1 Intro and Motivation

11.2 Machine Learning as a Service

11.3 Machine Learning in Python

11.3.1 Other packages

11.4 Other Frameworks

11.4.1 Other Open Source Frameworks

11.4.2 Proprietary solutions

11.5 Summary

12 Social aspects

12.1 Intro and motivation

12.2 Incentives for discussion

12.3 Robot laws

12.4 Biased Data and Potentials

12.5 Technological Singularity and the Big Questions

12.6 Supporting instead of replacing

12.7 Data protection and privacy

12.8 Learning

Classroom training: Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
Hybrid training: Hybrid training means that online participants can additionally attend a classroom course. The dynamics of a real seminar are maintained, and the online participants are able to benefit from that. Online participants of a hybrid course use a collaboration platform, such as WebEx Training Center or Saba Meeting. To do this, a PC with browser and Internet access is required, as well as a headset and ideally a Web cam. In the seminar room, we use specially developed and customized audio- and video-technologies. This makes sure that the communication between all persons involved works in a convenient and fault-free way.
Online training: You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
Tailor-made courses: You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.

You can find the complete description of this course with dates and prices ready for download at as PDF.