1. Introduction to Python

• Getting to know the class
• Python history, installation on your computer, examples
• The first Hello World Python program, Python programming philosophy

2. Code versioning

• Basics of code versioning
• Git and GitHub
• Git installation and environment preparation, practice

3. Python basics

• int, float, boolean and mathematical operations
• string, their methods, formatting, slice
• if conditions, input(), range(), for loop
• list, tuple, set
• dictionary
• whileloop
• functions, None, lambda, recursion, local vs. global scope
• error types, management and their generation try/except, raise
• Intermediate knowledge test
• Working with documents, os and pathlib libraries, relative vs. absolute path, .txt, .json, .yaml formats, pickle library
• Managing Python packages/libraries, importing modules, Python virtual environments, generating .exe from a .py script

4. Python object-oriented programming

• Definition of classes and OOP, examples
• Class declaration, constructor, destructor, creation of class units/objects (instance).
• Class methods and their calls
• Class inheritance, advantages and disadvantages, polymorphism, creating an abstract class
• designing class structures and methods
• Use of object-oriented programming in data analysis, real examples
• Class static properties and methods

5. Python data analytics

• Principles of web scraping, BeautifulSoup and other libraries, legal restrictions and ethical issues
• Analysis of textual data, Regex
• Numpy library, working with multidimensional data, data masks
• DataTables, Pandas library, Series and DaraFrame
• Data visualization, Matplotlib, Seaborn libraries. Interactive graphs with the plotly library
• Visual data analysis and processing. OpenCV, Pillow libraries

6. Python work with databases

• What is a database, SQL vs. noSQL, examples
• Installing a MySQL or PostgreSQL database on your computer, practice with the application's UI
• Reading data from the database.
• Creating and editing tables, saving and deleting data in the SQL database
• Extracting data from a database with Python
• Data Transformation, Management and Analysis with SQL and Python (Pandas Library)

1. Theory of Machine Learning

• What is Machine Learning (ML), AI vs. ML, how it differs from traditional programming, ML project flow
• History of Machine Learning
• Types of learning (supervised, unsupervised/self-supervised, reinforcement) and examples of use
• Structured (categories, numbers, etc.) vs. unstructured data (text, sound, etc.)

2. Supervised learning

• Supervised learning and usage examples
• Classification problem, training data split (train-test split), model evaluation metrics
• Scikit-learn library. Linear models, K-Nearest Neighbors, Decision Trees, Bagging and Boosting methods. underfit vs. overfit
• Binary vs. Multiclass vs. Multi-label classification. Logistic function
• Regression, regression model estimation metrics
• Practice with regression models (Scikit-learn library)
• Non-negative regression and example situations. ReLu function, Poisson and Tweedie data distributions

3. Unsupervised

• Unsupervised (self-supervised) learning and usage examples
• Data clustering, what is good clustering?

4. Feature engineering

• Data normalization, data encoding and why it is important
• Feature selection and Dimensionality reduction. Principal Component Analysis
• Model hyperparameter tuning (hyperparameter tuning), cross-validation

5. Evolutionary algorithms

• Theory and terminology of evolutionary algorithms
• Practice with Evolutionary Algorithms. Python DEAP library

You need a computer with an NVidia GPU with a Compute Capability of at least 6.0
or use Google Colab with GPU (free version doesn't always work due to busyness)

1. Theory of artificial neural networks (ANN).

• History of artificial neural networks, examples of use, what is an artificial neuron, activation functions
• Structure of artificial neural network (Multilayer Perceptron), foward propogation, backpropogation
• Loss/cost function, its representation as a mountainous surface, Gradient descent
• Learning cycle

2. ANN Practice with Pytorch

• Introducing and recording Pytorch
• Building an Artificial Neural Network with Pytorch to Classify Fashion-MNIST Photos. Model training, evaluation of results
• Loss curve, early stopping, how useful is Dropout

3. Convolutional Neural Networks (CNN)

• What is a photo for a computer? What problems do you have when trying to understand what is shown in the picture
• Filters (image kernel), convolutional network structure, pooling layer
• Building a CNN with Pytorch to classify photos (eg CIFAR10). Model training, evaluation of results
• Data augmentation and its impact on training
• Transfer Learning. How to adapt trained models to your needs
• Object detection in photos. YOLO, MobileNet models. Object recognition model evaluation metrics
• Semantic segmentation vs. instance segmentation. Mask R-CNN, Segment Anything (SAM), YOLACT models

4. Recurrent Neural Networks (RNN)

• Data streams (audio, video, text) and looking back
• Definition, uses and limitations of RNN network
• Music generation with RNNs

5. Generative Adversarial Networks (GANs)

• GAN definition, usage examples
• Training a GAN model to restyle a photo into a Van Gogh style

6. Transformer neural network (Transformer)

• Transformer definition, how data importance (attention) is calculated, usage examples, what are the disadvantages of this model
• Training a Transformer model for photo classification

1. NLP

• NLP Basics, History and Uses
• TF-IDF, bag of words, Spacy library
• Word embeddings, their types, pluses and minuses
• Text classification
• Transformer networks of text. Hugging face platform and library
• Text generation with Transformer networks

1. Reinforcement learning

• What is Reinforcement learning, history, usage examples, reward function, Exploration vs. Exploitation, action selection
• Reinforcement learning model training in one of the Gymnasium environments

1. Task solutions and consultations

• Solutions for tasks to be solved throughout the course, consultations for the final project

1. Final project presentations

• Final project presentations

1. Communication skills

• Active listening
• Effective communication
• Dealing with difficult customers
• Creating a connection

2. Problem solving strategies

• Problem analysis and diagnostics
• Determination of solutions
• Troubleshooting methods
• Root cause analysis of the problem
• Latest problem solving technologies
• How to get feedback from managers
• Professional development

3. Time management and organization

• Prioritization of tasks
• Workload management
• Delegation and collaboration

4. Emotional intelligence

• Understanding and managing emotions
• Empathy and compassion
• Conflict resolution
• Stress management

5. How to learn more and faster and less
to forget

•  5 minute brain exercise
• Clear your "mental fog" with these 5
  delicious brain foods
•  Learn a powerful memory technique to
  you would never miss a meeting

• The operation of artificial intelligence. How is it different from a human?
• Creating a request (Prompt). What is the difference between a good query and a bad one?
• Validation of results. Checking the received data for suitability in a specific case
• Versioning Policy. Identification of the used software (vendor) version in the request
• Artificial intelligence plugins in the code editor (IDE). Advantages and disadvantages
• Generating comments using artificial intelligence
• Creating tests with the help of a plugin
• Code optimization with the help of a plugin