AI Consultant

Modules, packages and error handling (approx. 4 days)

Introduction to Python modules and packages

Importing and using standard and third-party packages

Creating custom modules and packages

Working with sys and os (host platform functions)

Introduction to exceptions and error handling (try, except, finally)

Creating and using self-defined exceptions

Best practices for robust error handling

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Strings and OOP basics (approx. 8 days)

Introduction to working with strings

Integrated string methods (split, join, find, replace etc.)

Formatting and processing strings

String slicing and working with regular expressions (RegEx)

Introduction to classes, objects, instance methods and variables

Encapsulation, inheritance and polymorphism

Constructors (__init__) and destructors (__del__)

Inheritance hierarchies and superclasses

In-depth study of object-oriented programming (approx. 2.5 days)

In-depth study of inheritance and polymorphism

Application of magic methods (__str__, __repr__, __eq__, __lt__, etc.)

Properties and decorators in classes

Design patterns: singleton, factory, etc.

List Comprehensions for efficient list processing

Lambda functions and anonymous function writing

Closures and scoping in Python

Understanding and using generators and iterators

Working with files, databases and web development (approx. 2.5 days)

Reading and writing files (CSV, JSON)

Introduction to SQL and connection to SQLite databases

CRUD operations in a database (create, read, update, delete)

Introduction to Flask and creation of a simple web application

Routes and templates in Flask

CRUD applications in Flask (database integration)

Project work, certification preparation and certification exam "PCAP™ - Certified Associate Python Programmer" in English (approx. 3 days)

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