Python Machine Learning Immersive

1. Course Kick-off & Python Refresher

• Data Science tool recap - Pandas and indexing
• Exploratory data analysis (EDA): standard deviations and uniform vs. normal distributions using NumPy/Pandas
• Hands-on: loading CSVs, basic plotting with Matplotlib

2. Data Visualization & Simple Linear Regression

• Crafting clear scatterplots: labels, grids, styling
• Single-variable linear regression (attendance → concessions)
• Train-test splitting and dealing with outliers
• Evaluating models with R²; interpreting residuals
• Extended example: car-sales dataset, predicting price from one feature

3. Binary Classification & Logistic Regression

• From regression to classification: why logistic vs. linear
• Implementing logistic regression on an employee “stay/leave” dataset
• Classification metrics deep dive: accuracy, precision, recall, F1 score, ROC curve
• Understanding variability: train-test ratios, data shuffling, sample size effects
• Confusion matrix analysis

4. k-Nearest Neighbors & the Iris Dataset

• Introduction to k-NN: distance metrics, choosing k
• Dataset exploration: sepal/petal measurements, plotting clusters
• Preprocessing: label encoding categorical data, feature scaling
• Model training, hyperparameter tuning, evaluating with confusion matrix and classification report
• Brief intro to decision-tree logic (setting up for ensembles)

5. Ensemble Methods & Neural Networks

• Random forest classifiers on the Titanic dataset: feature engineering, importance scores
• Kaggle workflow: generating predictions, submitting to competition
• Neural network primer: perceptron to multilayer architectures
• Hands-on MNIST digit classification with Keras/TensorFlow in Colab

To reschedule or cancel, email us at [email protected]

All courses include a non-refundable registration fee (10% of the undiscounted course price).

• Students may cancel up to 11 business days before the class/program start date and receive a refund, less the registration fee.
• Cancellations within 11 business days are not permitted; however, students may reschedule up to 4 business days before the class start date.

Note: Any refunds must be requested within 180 days from the original payment date; courses rescheduled within 11 business days of the start date are not eligible for refunds.