Over the next three months, I’ll be diving into DataTalks.Club’s ML Zoomcamp, a course designed to deepen my skills in machine learning for finance. I’ll be sharing key insights, projects, and code here with you all! 📘✨
- 🗒️ Regression: For predicting financial trends, like asset prices or loan performance.
- 🗓️ Classification: Identifying risks or customer churn, key for risk management and retention.
- 🧮 Evaluation Metrics: Optimizing precision, recall, and more for financial model accuracy.
- ♻️Model Deployment: Building pipelines for production-ready models in finance.
- 🌳 Decision Trees & Ensemble Learning: Robust models for portfolio risk and investment strategy analysis.
- 🤖 Neural Networks: Modeling complex financial patterns like stock movements.
- ☁️ Serverless Deep Learning: Scalable deployments for real-time transaction analysis.
- 🛠️ Kubernetes: Efficient deployment for high-volume financial data.
I’m committed to Learning in Public—sharing detailed notes, real-world code samples, and financial ML case studies. This repository documents each step of my journey, combining practical projects with theoretical insights for a comprehensive view of machine learning in finance.
For an in-depth look at my machine learning journey, visit my Notion Page, where I document each skill, reflect on learning milestones, and provide additional resources. This page includes:
- In-depth explanations of machine learning concepts
- Personal reflections and milestones throughout my journey
- Additional resources to deepen understanding in finance-focused machine learning
Together, this repository and my Notion page offer a complete view of my ML learning experience, bridging theory with real-world application.
This journey is powered by DataTalks.Club. If you’re also learning ML, join the DataTalks.Club Slack (#course-ml-zoomcamp) for insights, support, and collaboration.
Let’s make an impact in financial data analysis together! 💥