JAK PRZYGOTOWAĆ DANE DO UCZENIA MASZYNOWEGO? 📊 MACHINE LEARNING W PRAKTYCE

☕ Let's grab a coffee and support my work: https://buycoffee.to/ksieradzinski 🐍 Learn the secrets of Python from scratch! Start your programming adventure: 👉 https://pystart.pl 🤖 Create AI apps! Enter the world of artificial intelligence with our course: 👉 https://dokodu.it/kursy/openaidev How to prepare data for machine learning? In this video, we discuss the key steps necessary for working with data for machine learning projects. You'll learn how to collect, clean, and transform data to make your models perform better and be more accurate. 🎯 What will you learn in this episode? How to collect data from various sources (databases, CSV, scraping). The data cleaning process: removing missing values ​​and outliers. Normalization and standardization – when to use them and what are their applications? Feature Engineering: Creating new features, such as total price. Splitting data into training and test sets – why is it important? 💡 Practical Python: Collecting and loading data using pandas. Cleaning data and preparing it for analysis. Using pandas and scikit-learn libraries for normalization and standardization. Creating new features and extracting information from data (e.g., cities from addresses). 🔗 Useful links mentioned in the video: Kaggle – free databases for experiments https://www.kaggle.com/ Google Dataset Search – dataset search https://datasetsearch.research.google... US government public data https://data.gov/