Mastering Data Collection and Preprocessing for Machine Learning | Step-by-Step Tutorial

Welcome to this insightful video on Data Collection and Preprocessing for Machine Learning Models. In this tutorial, we dive deep into the critical aspects of data gathering and preparing your data for machine learning applications.

Timestamps:
- 0:00 - Introduction
- 1:43 - Getting Started with Google Colab
- 8:38 - The Importance of Data Preprocessing
- 14:50 - Uploading Data to Google Colab
- 17:29 - Exploring Data with Pandas
- 1:09:22 - Utilising Scikit-Learn for ML

Plus, we've got you covered with a list of resources to find data for your own projects, from Kaggle to government repositories and more:

- Kaggle
- UCI Machine Learning Repository
- Quandl
- World Bank Data
- IMF Data
- Google Dataset Search
- Amazon Web Services (AWS) Data Exchange
- Microsoft Azure Open Datasets
- FiveThirtyEight Data
- NOAA Data
- European Data Portal
- European Space Agency (ESA) Open Data
- OpenStreetMap
- United Nations Data
- NASA Open Data
- UNECE Data
- Healthcare Cost and Utilisation Project (HCUP) Data
- Google Trends Data
- Bureau of Labor Statistics (BLS) Data

Watch now to enrich your understanding of these fundamental steps in your machine learning journey. Don't forget to subscribe for more informative content!"

#MachineLearning
#DataScience
#DataPreprocessing
#DataCollection
#AI
#DataAnalysis
#DataMining
#Python
#ScikitLearn
#Pandas
#GoogleColab
#DataAnalytics
#Kaggle
#DataCleaning
#FeatureEngineering
#DataVisualization
#BigData
#MLTutorials
#DataQuality
#ArtificialIntelligence

[atomscientist]

Thanks very much lecturer. I really appreciate you. @Emmanuel

[atomscientist]

Will be waiting for the video on label and coding 😊