Machine Learning

🤖 Machine Learning for Finance – Full Course Track

Welcome to the Machine Learning track at QuantML — built for finance professionals who are ready to do more than just Excel trendlines and actually model something.

This course is focused on practical, finance-specific ML tools — not Kaggle competitions, not startup pitch decks, and definitely not “just import scikit-learn and pray.”

You’ll learn how to:

• Frame financial problems as ML problems

• Engineer features from messy business data

• Train, validate, and deploy predictive models

• Use ML for forecasting, classification, segmentation, and risk

• Automate reports and commentary using ML + GPT

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🧱 How This Track Works

This is a hands-on course with real financial data and projects. Each module includes:

• 🧪 Live Python notebooks

• 💻 Real datasets (finance, ops, ERP, market)

• 📹 Optional walkthroughs

• 🧰 Reusable templates + deployable projects

All of it is modular — start anywhere, or follow the roadmap top to bottom.

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🧠 What You’ll Learn

You’ll go from:

“Isn’t machine learning just a fancy VLOOKUP?”

to

“I trained a model to predict revenue drop-offs and flagged risky deals in advance.”

You’ll build:

• Forecasting tools that actually learn patterns

• Churn, fraud, and risk models for internal use

• GPT-enhanced reporting tools using ML outputs

• Market prediction models that don’t rely on horoscopes

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🔥 Full Course Modules

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✅ Module 0 – ML Foundations for Finance

Goal: Learn what ML is and how it applies to real finance use cases

Outcome: Understand key concepts, workflows, and how to not embarrass yourself in meetings

Lessons:

• What is ML? Supervised vs Unsupervised learning

• Regression vs Classification

• ML lifecycle: data prep, training, validation, deployment

• Common finance use cases (forecasting, scoring, anomaly detection)

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✅ Module 1 – Data Prep & Feature Engineering

Goal: Clean, transform, and extract insights from raw data

Outcome: Build a training dataset your model won’t choke on

Lessons:

• Data cleaning and outlier handling with pandas

• Feature engineering (lag features, ratios, growth %)

• Encoding categories and handling nulls

• Splitting time series vs random sampling

• Data leakage: how to not cheat without realizing it

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✅ Module 2 – Regression Models in Finance

Goal: Predict continuous values like revenue, margins, or costs

Outcome: Build forecasts your manager can’t ignore

Lessons:

• Linear regression with real finance data

• Regularization: Ridge, Lasso (and why they matter)

• Decision Tree and Random Forest regressors

• Use case: Forecasting sales / EBITDA / costs

• Evaluation: RMSE, MAE, MAPE

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✅ Module 3 – Classification Models for Risk & Churn

Goal: Predict binary outcomes like fraud, churn, or defaults

Outcome: Flag risky deals, fake invoices, or sketchy clients

Lessons:

• Logistic regression

• Decision Trees, Random Forest, XGBoost

• Use case: Predicting customer churn / risk scoring

• Precision, Recall, ROC-AUC explained like you're five

• Model calibration and thresholds

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✅ Module 4 – Time Series Forecasting

Goal: Predict future values using past trends

Outcome: Build forecasts with actual time awareness

Lessons:

• Stationarity, trends, seasonality

• ARIMA and SARIMA with statsmodels

• Facebook Prophet for easy forecasting

• Model comparison with rolling forecasts

• Use case: Monthly revenue forecast for business unit

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✅ Module 5 – Unsupervised Learning for Segmentation

Goal: Group customers, vendors, or deals with no labels

Outcome: Find patterns Excel never could

Lessons:

• K-means clustering with financial data

• PCA and dimensionality reduction

• Use case: Customer segmentation, spend clustering

• Visualizing clusters and interpreting patterns

• Risk-based portfolio segmentation

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✅ Module 6 – GPT + ML for Reporting

Goal: Turn ML predictions into smart, readable explanations

Outcome: Automate the commentary no one wants to write

Lessons:

• Connect GPT to ML outputs

• Explain model predictions in plain English

• Auto-generate commentary for dashboards

• Use case: “Why did revenue drop?” — GPT tells you using model outputs

• Prompt engineering with ML context

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✅ Module 7 – Building a Finance ML App

Goal: Package your model into something people can actually use

Outcome: Deploy a tool your team can click, not code

Lessons:

• Streamlit app structure for ML tools

• Creating input forms and uploading data

• Displaying predictions and GPT-generated summaries

• Deploying to Streamlit Cloud

• Use case: Forecasting app for finance leadership

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✅ Module 8 – Final Capstone Projects

Goal: Apply everything into full-blown, portfolio-worthy tools

Outcome: Impress recruiters, scare coworkers, save time

Project Ideas:

• Revenue Forecaster: Inputs from CSV, ML + GPT commentary, Streamlit interface

• Churn Predictor: Client-level risk analysis with ROC curve dashboard

• Cost Anomaly Detector: Flagging sketchy spend spikes

• ML-Powered Budget Variance Analyzer — Predict next month's numbers and explain the gap

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🎯 Who This Is For

This track is ideal for:

• FP&A professionals ready to level up

• Data-curious finance pros and analysts

• MBA/CFA students building a serious resume

• BI consultants transitioning to data science

• People sick of fake “AI in finance” fluff who want to actually build stuff

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👇 Get Started

Subscribe, copy some code, and pretend you’ve always known what “regularization” meant.

👉 Subscribe to QuantML

Or go back to dragging down Excel rows. That’s fine too. Some people prefer suffering.

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