Machine learning
Supervised and Unsupervised Learning Models
In this blog, we will discuss supervised and unsupervised learning models. We will cover
a bit of the logic and math behind them, and then explain how to program them on
Google CoLab.
Supervised Learning
Uses labeled data to train a model
Model makes predictions on new, unlabeled data
Examples: Linear Regression, Logistic Regression, Decision Trees, Random Forests
Unsupervised Learning
Uses unlabeled data to find patterns and relationships
Examples: Clustering, Association Rule Learning, Dimensionality Reduction
Programming on Google CoLab
Google CoLab is a free online platform that allows you to write and run Python code. It
also provides access to GPUs and TPUs for faster computation. Here's how to get
started:
Create a new notebook
Import necessary libraries (e.g. numpy, pandas, sklearn)
Load your dataset
Preprocess your data (e.g. scaling, imputation, feature engineering)
Split your data into training and testing sets
Select a model and fit it to your training data
Evaluate your model on your testing data
Iterate on your model (e.g. hyperparameter tuning, feature selection)
Conclusion
Supervised and unsupervised learning models are powerful tools for analyzing data. By
understanding the logic and math behind these models, and learning how to program
them on Google CoLab, you can unlock new insights from your data
Supervised and Unsupervised Learning Models
In this blog, we will discuss supervised and unsupervised learning models. We will cover
a bit of the logic and math behind them, and then explain how to program them on
Google CoLab.
Supervised Learning
Uses labeled data to train a model
Model makes predictions on new, unlabeled data
Examples: Linear Regression, Logistic Regression, Decision Trees, Random Forests
Unsupervised Learning
Uses unlabeled data to find patterns and relationships
Examples: Clustering, Association Rule Learning, Dimensionality Reduction
Programming on Google CoLab
Google CoLab is a free online platform that allows you to write and run Python code. It
also provides access to GPUs and TPUs for faster computation. Here's how to get
started:
Create a new notebook
Import necessary libraries (e.g. numpy, pandas, sklearn)
Load your dataset
Preprocess your data (e.g. scaling, imputation, feature engineering)
Split your data into training and testing sets
Select a model and fit it to your training data
Evaluate your model on your testing data
Iterate on your model (e.g. hyperparameter tuning, feature selection)
Conclusion
Supervised and unsupervised learning models are powerful tools for analyzing data. By
understanding the logic and math behind these models, and learning how to program
them on Google CoLab, you can unlock new insights from your data
