A collection of hands-on projects completed as part of the Post Graduate Program in AI & Machine Learning — Great Lakes Executive Learning × UT Austin McCombs (2021).
All projects are implemented in Python using Jupyter Notebooks.
Exploratory data analysis, hypothesis testing, probability distributions, and statistical inference. Key techniques: descriptive statistics, correlation analysis, A/B testing.
Classification and regression models on real-world datasets. Key techniques: Linear/Logistic Regression, Decision Trees, SVMs, KNN, model evaluation metrics.
Clustering and dimensionality reduction for pattern discovery. Key techniques: K-Means, Hierarchical Clustering, PCA, t-SNE.
Improving model performance using ensemble methods. Key techniques: Random Forest, Bagging, Boosting, XGBoost, AdaBoost, Stacking.
Building and training deep neural networks from scratch and using Keras/TensorFlow. Key techniques: Feedforward networks, Backpropagation, CNNs, Regularization, Hyperparameter tuning.
Image classification and object recognition using deep learning. Key techniques: Convolutional Neural Networks (CNNs), Transfer Learning, Data Augmentation.
- Python 3
- Jupyter Notebooks
- scikit-learn · TensorFlow · Keras
- Pandas · NumPy · Matplotlib · Seaborn
The PGP in AI & ML is a rigorous 1-year program jointly delivered by Great Lakes Executive Learning and UT Austin McCombs School of Business, covering the full spectrum of classical ML through advanced deep learning.