Machine Learning & AI Training

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

• The "Machine Learning & AI" training is a course aimed at providing participants with practical skills related to artificial intelligence (AI). This training aims to prepare specialists or employees from various industries to utilize AI technology in their work or projects. Such training is becoming increasingly popular as AI becomes more prevalent in many fields, and there is a demand for specialists with skills in this area. It is worth noting that AI is a very dynamic field, so regular training and updating knowledge are crucial for maintaining professional competitiveness.
• The training will be based on the Python language and popular libraries such as: pandas, numpy, scikit-learn, pytorch, and others. The training will be conducted on the Google Colaboratory platform, and the requirement for participants is to have a regular Google account, e.g., Gmail.

• IT specialists and programmers looking to expand their skills in programming in the field of machine learning and AI.
• Engineers working in fields such as robotics, automation, or electronics, utilizing knowledge about implementing AI systems in their projects.
• Data analysts, both beginners and experienced, who want to learn data analysis techniques using AI methods, allowing for more advanced analysis and inference from data.

• You will understand the role of data in machine learning and the principles of data preparation.
• Practical application of learned algorithms in solving problems.
• You will learn about types of machine learning and basic algorithms in the specified areas.
• Preparation for independently creating a POC.

1. DAY 1 – DATA
    • Warm-up – what is Machine Learning (ML)
     • Definition – basic concept of Machine Learning and the difference between traditional programming and machine learning
     • History and development – evolution of ML and its impact on industry and science
     • Types of learning – supervised, unsupervised, and reinforcement learning
    • Data – EDA (Exploratory Data Analysis) and preprocessing
     • Importance of data – why data is important and what its sources are
     • Exploratory analysis (EDA) – visualizations, descriptive statistics, outlier detection
     • Data preparation – cleaning, encoding categorical variables, scaling, normalization, splitting into training/test sets
     • Practical data preparation for machine learning – working with real data (Google Colaboratory, Python)

2. DAY 2 – SUPERVISED LEARNING
    • Definition – characteristics, applications, advantages, and disadvantages
    • Regression – linear, polynomial, and logistic models
    • Classification – decision trees, support vector machines (SVM), k-nearest neighbors (k-NN)
    • Evaluation metrics – mean squared error, precision, sensitivity, F1, ROC curve, AUC
    • Practical application of algorithms – creating a POC with real data (Google Colaboratory, Python)

3. DAY 3 – UNSUPERVISED LEARNING
    • Definition – characteristics, applications, advantages, and disadvantages
    • Clustering – k-means, DBSCAN, hierarchical clustering methods
    • Dimensionality reduction – PCA, t-SNE
    • Practical application of algorithms – creating a POC with real data (Google Colaboratory, Python)

4. DAY 4 – NEURAL NETWORKS
    • Introduction – what neural networks are, their applications, advantages, and disadvantages
    • Basics – perceptron, network architecture, activation functions, forward and backward propagation
    • Deep learning – deep neural networks, convolutional networks (CNN) for image analysis
    • Practical application of algorithms – creating a POC with real data (Google Colaboratory, Python)

32 h/ 4 days