Data Collection for Machine Learning | AI Beginner's Guide скачать в хорошем качестве

Data Collection for Machine Learning | AI Beginner's Guide

How do you ensure your machine-learning models are as accurate as possible? The answer lies in data collection for machine learning! Discover the essential steps—from web scraping to cross-validation—that make all the difference in building powerful ML models. 🏠 Check out Decodo (formerly Smartproxy) web scraping proxies: https://decodo.com/proxies/residentia... Or explore our ready-made web scraping tools: https://decodo.com/scraping?utm_sourc... Python web scraping tutorials:    • Python Web Scraping Tutorials   📚 Questions answered in this video 00:00 What is Data Collection for Machine Learning? 01:34 Different Types of Data Explained 02:36 Web Scraping for Machine Learning 04:02 How to Ensure Data Quality? 05:14 Avoiding AI Hallucinations: Why Data Sources Matter 05:48 Supervised vs. Unsupervised Learning: Data Labeling Explained 06:57 Cross-Validation: How to Test Your Model Data collection is crucial for any ML project. Whether you're looking to understand data collection for machine learning or need tips on how to gather data for machine learning, this video is for you. Don't miss out on learning how to improve your ML models with better data! Some FAQ: ❓ What is data collection for machine learning? Data collection involves gathering raw data from various sources to create a large, diverse, and relevant dataset that can be used to train machine learning models. ❓ How do I collect data for machine learning? Depending on the project's needs, data can be collected through surveys, collaboration with research labs, public engagement initiatives, or web scraping for data science. ❓ What is web scraping in machine learning? Web scraping is extracting data from websites using custom-made code, web scraping libraries, APIs, or codeless tools. It's a powerful technique for gathering public web data at scale. ❓ How can I ensure data quality? Ensure data quality by focusing on accuracy, completeness, consistency, and relevance. Regular data cleansing and careful source selection are crucial to preventing issues like AI hallucinations. ❓ What are AI hallucinations? AI hallucinations occur when a model produces misleading or false results due to poor-quality or inaccurate training data, leading to what's known as a "model collapse." ❓ What's the difference between supervised and unsupervised learning? Supervised learning uses labeled data to recognize existing patterns and solve known problems, while unsupervised learning explores data to find patterns without predefined labels.