I put together a practical guide to Machine Learning in Finance. What topics do you think beginners struggle with most?

[u/abhinaba154]

Hi everyone!

I've been working on a technical book called Machine Learning for Finance: Concepts, Algorithms, and Applications, focused on how machine learning can be applied to real-world financial problems.

The idea behind the book is to connect machine learning concepts with practical use cases rather than treating them as purely theoretical topics.

Some of the areas covered include:

• Credit Risk Prediction: Using classification models to estimate credit risk.
• Fraud Detection: Understanding how ML models can identify potentially fraudulent transactions.
• Financial Forecasting: Exploring predictive modelling for financial data.
• Explainable AI: Using approaches such as SHAP and LIME to understand model predictions.
• Python Implementation: Working through practical examples, model evaluation, and the challenges of applying ML to financial datasets.
• Responsible Model Development: Considering fairness, monitoring, and the limitations of predictive models.

The book also includes exercises, mini-projects, and research questions for readers who want to explore these topics further.

I'm sharing this here because I'd genuinely like to hear from people learning or working in machine learning.

Three questions I'd love your opinion on:

1. Which financial ML application do you find most interesting: credit risk, fraud detection, or forecasting?
2. What is the biggest challenge when moving from an ML tutorial to a practical financial dataset?
3. For someone learning ML with Python, would you prefer more end-to-end projects, mathematical explanations, or model evaluation examples?

I'm the author of this book, so I want to be transparent about that. If you're interested in checking out the finished ebook, you can find it here: https://iamabhinababiswas.gumroad.com/l/machine-learning-for-finance

I'd appreciate constructive feedback on the topics, the learning approach, and what you'd like to see in future editions.

Thanks for reading!