Machine Learning Developer

Introduction to data analysis (approx. 1 day)

CRISP-DM reference model

Data analytics workflows

Definition of artificial intelligence, machine learning, deep learning

Requirements and role in the company of data engineers, data scientists and data analysts

Review of Python basics (approx. 1 day)

data types

Functions

Data analysis (approx. 3 days)

Central Python modules in the context of data analytics (NumPy, Pandas)

Process of data preparation

Data mining algorithms in Python

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Data visualization (approx. 3 days)

Explorative data analysis

insights

Data quality

Benefit analysis

Visualization with Python: Matplotlib, Seaborn, Plotly Express

Data storytelling

Data management (approx. 2 days)

Big data architectures

Relational databases with SQL

Comparison of SQL and NoSQL databases

Business Intelligence

Data protection in the context of data analysis

Data analysis in a big data context (approx. 1 day)

MapReduce approach

Spark

NoSQL

Dashboards (approx. 3 days)

Library: Dash

Structure of dashboards - Dash components

Customizing dashboards

Callbacks

Text Mining (approx. 1 day)

Data preprocessing

Visualization

Library: SpaCy

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

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

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