🎙 Join DeepStation for an exciting session on “Introduction to Machine Learning” featuring Leandro Lima, Machine Learning Engineer at Block (ex-Meta)!
🚀 Ever wondered how Instagram knows what you’ll like or how banks decide loan approvals? This hands-on introduction breaks down these “magic” algorithms into understandable concepts anyone can grasp. Through interactive video lessons and real-world coding exercises using Python, you’ll go from complete beginner to confidently building your first predictive models.
💡 You’ll learn:
🔹 Essential ML foundations: classification, regression, clustering, and neural networks
🔹 How to work with genuine datasets to solve practical problems like predicting prices or detecting spam
🔹 Visual learning through interactive animations showing algorithms work in real-time
👤 Bio:
Leandro Lima is a PhD with background in Computer Science and several years of experience in AI/ML. After spending 2 years at Meta, he recently joined Block (Square/Cash App) where he has worked with Personalization ML, LLMs and Block’s AI agent (Goose). He was also a CS lecturer for 4 years in Brazil.
🎓 Perfect for beginners with basic programming skills looking to enter the world of machine learning and understand the algorithms behind everyday technology.
00:00:00 – Welcome + Series Kickoff
00:00:25 – Meet Leandro: Ex-Meta, Now at Block
00:02:03 – How Recommenders Work (YouTube/Netflix)
00:03:48 – Data 101: Features, Rows, and Tables
00:06:11 – Hidden Patterns: X, Y, Z Relationships
00:14:24 – Linear Regression: The Big Idea
00:17:03 – y = mx + b Explained Simply
00:19:21 – Error 101: MSE vs MAE
00:26:12 – Train/Test Split + Data Leakage
00:31:40 – Regression vs Classification
00:35:14 – Iris Dataset: 4 Features, 3 Species
00:38:27 – Petal Power: Easy Separators + Thresholds
00:43:45 – Confusion Matrix Made Simple
00:54:55 – Precision vs Recall (What to Optimize)
00:56:29 – What’s Next: Visualizing Iris in Code
00:57:33 – 10+ Years in ML: Career Snapshot
00:58:51 – From Notebook to MLOps: Real-World ML
01:01:08 – Breaking In: Projects, Practice, Patience
01:03:15 – Why ML Feels Like Magic (Real Use Cases)
01:04:16 – Closing Thoughts + Next Episode
