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Certificates: Certificate "Big Data Specialist"
Certificate "AI specialist" -
Additional Certificates: Machine Learning" certificate
Deep Learning" certificate -
Examination: Practical project work with final presentations
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Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 12 Weeks
Big Data Specialist
What is Big Data? (approx. 1 day)
Volume, Velocity, Variety, Value, Veracity
Opportunities and risks of large amounts of data
Differentiation: business intelligence, data analytics, data science
What is data mining?
Introduction to Apache Frameworks (approx. 2 days)
Big data solutions in the cloud
Data access patterns
Data storage
MapReduce (approx. 3 days)
MapReduce philosophy
Hadoop Cluster
Chaining of MapReduce jobs
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Components (approx. 3 days)
Brief presentation of various tools
Data transfer
YARN applications
Hadoop JAVA-API
Apache Spark
NoSQL and HBase (approx. 3 days)
CAP theorem
ACID and BASE
Types of databases
HBase
Big Data Visualization (approx. 3 days)
Theories of visualization
Diagram selection
New types of diagrams
Tools for data visualization
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
Machine Learning
Introduction to Machine Learning (approx. 5 days)
Why machine learning?
Application examples
Supervised learning, unsupervised learning, partially supervised learning, reinforcement learning
Examples of data sets
Getting to know data
Training, validation and test data
Viewing data
Making predictions
Supervised learning (approx. 5 days)
Classification and regression
Generalization, overfitting and underfitting
Size of the data set
Algorithms for supervised learning
Linear models
Bayes classifiers
Decision trees
Random Forest
Gradient Boosting
k-nearest neighbors
Support Vector Machines
Conditional Random Field
Neural Networks and Deep Learning
Probabilities
Unsupervised learning (approx. 5 days)
Types of unsupervised learning
Preprocessing and scaling
Data transformations
Scaling training and test data
Dimension reduction
Feature engineering
Manifold learning
Principal component decomposition (PCA)
Non-negative matrix factorization (NMF)
Manifold learning with t-SNE
Cluster analysis
k-Means clustering
Agglomerative clustering
Hierarchical cluster analysis
DBSCAN
Cluster algorithms
Evaluation and improvement (approx. 2 days)
Model selection and model evaluation
Tuning the hyperparameters of an estimator
Cross-validation
Grid search
Evaluation metrics
Classification
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Deep learning
Introduction to Deep Learning (approx. 1 day)
Deep learning as a type of machine learning
Fundamentals of neural networks (approx. 4 days)
Perceptron
Calculation of neural networks
Optimization of model parameters, backpropagation
Deep learning libraries
Regression vs. classification
Learning curves, overfitting and regularization
Hyperparameter optimization
Stochastic gradient descent (SGD)
Momentum, Adam Optimizer
Learning rate
Convolutional Neural Network (CNN) (approx. 2 days)
Image classification
Convolutional layers, pooling layers
Reshaping layers, flattening, global-average pooling
CNN architectures ImageNet-Competition
Deep neural networks, vanishing gradients, skip connections, batch normalization
Transfer Learning (approx. 1 day)
Adaptation of models
Unsupervised pre-training
Image data augmentation, explainable AI
Regional CNN (approx. 1 day)
Object localization
Regression problems
Branched neural networks
Methods of creative image generation (approx. 1 day)
Generative Adversarial Networks (GAN)
Deepfakes
Diffusion models
Recurrent neural networks (approx. 2 days)
Sequence analysis
Recurrent layers
Backpropagation through time (BPTT)
Analysis of time series
Exploding and vanishing gradient problems
LSTM (Long Short-Term Memory)
GRU (Gated Recurrent Unit)
Deep RNN
Deep LSTM
Text processing using neural networks (approx. 2 days)
Text preprocessing
Embedding layers
Text classification
Sentiment analysis
Transfer learning in NLP
Translations
Sequence-to-sequence method, encoder-decoder architecture
Language models (approx. 1 day)
BERT, GPT
Attention layers, Transformers
Text generation pipelines
Summarization
chatbots
Deep reinforcement learning (approx. 1 day)
Control of dynamic systems
Agent systems
Training through rewards
Policy Gradients
Deep Q-learning
Bayesian neural networks (approx. 1 day)
Uncertainties in neural networks
Statistical evaluation of forecasts
Confidence, standard deviation
Unbalanced data
Sampling methods
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Changes are possible. The course content is updated regularly.
After the course, you will be able to process large, unstructured data volumes with the help of industry-specific software. You will have knowledge of the Apache framework and know how to visualize data in an appealing way.
You also have relevant knowledge of machine learning and deep learning. You know the most important reasons for using machine learning, areas of application and the various categories and concepts of machine learning. You also understand the areas of application of deep learning and how neural networks work. You will be able to provide machine learning and document processes.
The course is aimed at people with a degree in computer science, business informatics, mathematics or a comparable qualification.
As companies have to manage and structure ever-increasing volumes of data to evaluate and set objectives for their business processes, data processing skills are in demand in all sectors.
As an AI specialist in the fields of machine learning and deep learning, you are also highly qualified, can be deployed across all industries and are therefore in high demand on the job market. You can analyze large amounts of data for patterns and models. Deep learning is often used in the context of artificial intelligence for face, object or speech recognition.
Your meaningful certificate provides a detailed insight into the qualifications you have acquired and improves your career prospects.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).