Michał Żarnecki Portfolio

E-learning course: Machine learning – how to use the potential of data to get better results and make smart decisions

Posted on 3 January 2024  in lectures

Course scenario:

1. Definition and applications of machine learning
   • Data deluge and the definition of machine learning
   • Machine learning examples and related fields of knowledge
   • Types of machine learning
2. Machine learning tools used in the course
   • Programs used in the course
   • Orange Data Mining
   • Jupyter Lab
3. Supervised machine learning
   • Machine learning process
   • Data collection, labeling and analysis
   • Feature engineering and division into training and testing sets
   • Model training and evaluation
   • Model export, corrective actions
   • Regression example
   • Classification example

AWS, CNN, langchain, LLM, Machine Learning, NER, NLP, Python, RAG, reinforcement learning, RNN, ScikitLearn, supervised ML, tensorflow, time series, unsupervised ML

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PHPCon Conference 17-18.11.2023 Zawiercie

Posted on 18 November 2023  in events

ML with PHP – replace complex business logic with machine learning models

Abstract: Have you ever encountered code with so many conditions and processing paths that it was almost impossible to maintain and extend? What if we replaced it with an automatically generated, self-improving algorithm? In recent years, machine learning as a field of artificial intelligence has become an effective tool for creating systems and applications. With the development of artificial neural networks, programming complex business rules and services based on prediction and classification can be replaced by pre-trained machine learning models. In this presentation, you will see a case study illustrating the potential of PHP in integrating machine learning. We will walk through the process of creating a classifier and placing it in a PHP-based project.

Docker, Machine Learning, NER, PHP, Python, tensorflow

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PHPers Summit Conference 27.05.2023 Poznań

Posted on 27 May 2023  in events

Lecture: Exploring the viability of PHP for implementing artificial neural networks: A case study on autonomous vehicle control with CNN model

Abstract: In recent years, machine learning has become an essential tool for developing intelligent systems. With the rise of artificial neural networks, programming languages such as Python and R have become the go-to options for machine learning implementation. But is PHP a viable alternative? In this presentation, we will explore the potential of PHP for implementing artificial neural networks by examining its limitations compared to other popular languages. We will also demonstrate the application of machine learning in PHP through a case study where we trained a convolutional neural network model to control a prototype of an autonomous vehicle using Raspberry Pi and Nvidia’s “DAVE 2” CNN model architecture.

CNN, Machine Learning, PHP, tensorflow

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Self-driving vehicle based on tensorflow CNN and RasberryPi

Posted on 26 January 2023  in projects

Responsibilities:
– prepare laboratories for students related to computing vision recognition and training autonomus vehicle using convolutional neural network and tensorflow library
– assemble vehicles using Raspberry Pi 4 Model B, motors and other parts
– configure environment for model training and run model on Raspbian OS
– implement module for object detection

CNN, Machine Learning, OpenCV, Python, tensorflow

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Machine Learning – topic modelling for german companies description

Posted on 24 February 2019  in projects

Machine learning model and web service predicting company industry codes based on description.

Responsibilities:

• Implement machine learning model classyfing text into over 100 classes
• German text preprocessing and normalization
• Evaluation and, upgrading model parameters
• Implement web service for real time prediction

Machine Learning, Python, ScikitLearn

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System to collect networking and finance data about German companies

Posted on 1 July 2017  in projects

Pictures from moneyhouse.de

Web application to collect networking and finance data about German companies.

Responsibilities:

• Implementing data mining tools and parsers using deterministic algorithms and deep learning models
• creating fast and efficient search engine
• carrying out integration with external platforms, APIs, web-services
• working with Selenium, automation of acceptance, integration, functional and unit tests, TDD
• conducting data analysis using Python, R
• server environment setup and configuration

AWS, Cassandra, Data mining, data science, DBMS, Docker, Jenkins, Machine Learning, MongoDb, Neo4j, Neural networks, Nginx, NLP, NumPy, Pandas, PHP, regular expression, ScikitLearn, Server administration, Spacy, text mining, TextBlob, Ubuntu, Yii2

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