My lecture notes & resources from Machine Learning in Finance. It is more likely you will drown rather than starve when it comes to data science and ML resources. Nonetheless, access to all my lecture notes and notebooks are below.

Module Sub-Module Topic Description Material Type Filename
1-Link 1a ML in Finance Course movtivation:What is the state of Machine Learning and why is it important.  What are some use cases in Finance and how is it used in practice in the workplace PPT Intro to ML in Finance
FinTech Landscape Primer on the FinTech PPT FinTech Landscape
1b Introduction to Python Review of notebooks from [JVP] Chapters 1-3; also used [WM] Jupyter Notebook 0_intro_to_python
First sklearn model Based on [JVP], framework for instantiating, fitting, and predicting a model in Python Jupyter Notebook 1_first_model
Model Selection Description of hypterparameter selection using cross-validation; bias-variance trade-off. PPT Model Selection
Cross-validation illustrated Builds from: all in-sample, train-test split, cross-validation, gridsearch cross validation Jupyter Notebook 2_crossval
Bias-Variance Demonstrates concept of overfitting (low-bias, high variance) and the conceptual balance to found ideally through gridsearch cross validation Jupyter Notebook 3_bias_variance
2-Link 2a Linear Regression A review of linear regression derivation motivating regularized regression. PDF Linear Regression
2b Review of Value-at-Risk A review of Value-at-Risk to help the regularized regression use case. PPT Value-at-Risk
VaR hedging Demonstration of how one would find securities to hedge a portfolio VaR Jupyter Notebook VaR Hedging
2c Basket Replication Demonstration of how one would find securities to replicate an instrument. Jupyter Notebook Basket Replication
2-data Portfolio Prices Unknown time series (was a single name stock) representing a portfolio PnL. Data pnl_prices.csv
2-data Hedge Prices Single name stock prices available for shorting. Data hedge_prices.csv
3-Link 3a Decision Trees & Random Forests: Ensembles An overview from difference resources on Decision Trees and Random Forest PDF Ensembles.pdf
3b Decision Trees Notebook Adaptation from A Geron's notebook that accompanies his textbook Jupyter Notebook decision_trees
3c Ensemble & Random Forest Notebook Adaptation from A Geron's notebook that accompanies his textbook Jupyter Notebook ensembles_random_forest
3d Trading Model Framework for creating a Machine Learning trading strategy. Jupyter Notebook ML_trading_model
4-Link 4a Methods in Unsupervised Learning An overview of unsupervised learning, k-means and hierarchical clustering. PDF unsupervised_learning.pdf
4b FX Use Case Demonstration of clustering currencies by their average and std dev of returns. Jupyter Notebook kmeans_FXdata.ipynb
4c Agglomerative Clustering Demonstration of agglomerative clustering Jupyter Notebook hierarchical_clustering.ipynb
4d Hierarchical Risk Parity Implementation of Hierarchical Risk Parity. Jupyter Notebook hierarchical_risk_parity.ipynb