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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.
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Course Contents
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- 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
The Premium Print Package can be added during the ordering process for € 150,- plus VAT (only for classroom participation). -
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.

-
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
The Premium Print Package can be added during the ordering process for € 150,- plus VAT (only for classroom participation). -
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.
