Journey into Machine Learning with Sklearn and TensorflowFoundational Machine Learning Models with Sklearn

Learn fundamental machine learning models with Sklearn, centered on the Iris Dataset. This course covers key algorithms like linear and logistic regression, and decision trees. Master implementation, evaluation, and optimization to pave the way for advanced machine learning concepts.

Inspecting the Shape of the Iris Dataset

Loading the Iris Dataset from Sklearn

Splitting the Dataset into Training and Testing Data

Fixing the Dataset Splitting Code

Implementing a Linear Regression Model with 2 Features

Model fitting and evaluation

Assess Model Performance on Test Data

Splitting the Dataset into Training and Testing Data

Splitting the Dataset into Training and Testing Data

Generating Predictions using Logistic Regression Model

Tune the Logistic Regression Model

Implement Logistic Regression model training

Implement Decision Tree with Different Splitting Criterion

Add Visualization for Confusion Matrix

Decision Tree Tuning

Calculate Decision Tree Model Accuracy

Crunching Numbers: Calculating Mean Absolute Error

Precision in Logistic Regression Models

Evaluating Accuracy of Decision Tree Model

Implement Linear Regression on the Iris Dataset

Implementing Logistic Regression Model with Iris Dataset

"Improving Decision Tree Model with Parameter Tuning"

Adjust Hyperparameters and Optimize the Same Model

Adjust Hyperparameters and Optimize with RandomSearchCV

Enhance Decision Tree Classifier Performance

Enhance Decision Tree Performance with RandomizedSearch