-
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.
Request for customized courses
You can find the complete description of this course with dates and prices ready for download at as PDF.-
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.
Request for customized courses
You can find the complete description of this course with dates and prices ready for download at as PDF.
Classroom Training
Hybrid Training
Trainer language German