This blog is where I document my daily learning journey. Join me as I share new insights, skills, and discoveries, one day at a time. It’s a personal log of continuous growth.
Linear Regression, Homoscedasticity, Variance, Mean Squared Error
Linear regression is a statistical technique for modeling relationships between a dependent variable and independent variables to make predictions. It includes simple and multiple linear regression, relies on assumptions like homoscedasticity, and uses metrics like Mean Squared Error to evaluate performance. It’s widely applied in fields such as finance and biology.
Keep readingTrain-Test Split Explained for ML Models
In machine learning, data is divided into training and testing sets to evaluate model performance. A common split is 70-30% or 80-20%. Using Scikit-learn’s train_test_split allows random distribution while maintaining class proportions. The Iris dataset is used in the example, demonstrating the process and verifying class distributions.
Keep readingWhat is Data Processing for AI/ML?
Data processing is essential in AI and ML, transforming messy raw data into usable formats for algorithm effectiveness. Critical steps include data cleansing, integration, transformation, and feature engineering. Techniques for handling missing values, like using mean or median, and feature scaling, such as StandardScaler, improve model performance and prevent overfitting.
Keep readingUnderstanding AI, ML, NN, and DL: A Beginner’s Guide
The content discusses the concepts of Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN), and Deep Learning (DL), explaining their relationships and functions. It also includes instructions for installing key Python libraries for ML, verifying their installation, and demonstrates a simple operation using NumPy to confirm setup.
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