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Machine Learning

Data Science and Artificial Intelligence

ExperTeach Networking Logo

The topics of data science, artificial intelligence and machine learning form the backbone of every IoT or digitalization solution today, as this is where the real added value lies. This training course provides an insight into the methods and technologies that can be used to process, analyze and use digital data for self-learning business optimization. The course is geared towards the requirements of the industry and includes many interactive exercises that are designed for both direct users and decision-makers. This provides a well-founded and broad introduction to the fields of artificial intelligence and machine learning. The core skills required to develop new concepts or evaluate existing ones are taught and the practical relevance is established.

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

The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.

Premium Course Documents

In addition to the digital documentation package, the exclusive Premium Print Package is also available to you.

  • High-quality color prints of the ExperTeach documentation
  • Exclusive folder in an elegant design
  • Document pouch in backpack shape
  • Elegant LAMY ballpoint pen
  • Practical notepad
Premium Print
The Premium Print Package can be added during the ordering process for € 150,- plus VAT (only for classroom participation).

Request in-house training now

Target Group

The course is aimed at IT-savvy participants (from users to decision-makers) who are looking for a broad introduction to the topics of data science, artificial intelligence and machine learning for application.

Knowledge Prerequisites

Participants should have an understanding of and interest in the trending topics of digitalization, big data and IoT. Basic programming skills are an advantage.

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.
Request in-house training now
PDF SymbolYou can find the complete description of this course with dates and prices ready for download at as PDF.

The topics of data science, artificial intelligence and machine learning form the backbone of every IoT or digitalization solution today, as this is where the real added value lies. This training course provides an insight into the methods and technologies that can be used to process, analyze and use digital data for self-learning business optimization. The course is geared towards the requirements of the industry and includes many interactive exercises that are designed for both direct users and decision-makers. This provides a well-founded and broad introduction to the fields of artificial intelligence and machine learning. The core skills required to develop new concepts or evaluate existing ones are taught and the practical relevance is established.

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

The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.

Premium Course Documents

In addition to the digital documentation package, the exclusive Premium Print Package is also available to you.

  • High-quality color prints of the ExperTeach documentation
  • Exclusive folder in an elegant design
  • Document pouch in backpack shape
  • Elegant LAMY ballpoint pen
  • Practical notepad
Premium Print
The Premium Print Package can be added during the ordering process for € 150,- plus VAT (only for classroom participation).

Request in-house training now

Target Group

The course is aimed at IT-savvy participants (from users to decision-makers) who are looking for a broad introduction to the topics of data science, artificial intelligence and machine learning for application.

Knowledge Prerequisites

Participants should have an understanding of and interest in the trending topics of digitalization, big data and IoT. Basic programming skills are an advantage.

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.
Request in-house training now

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