Description
Deep Learning Prerequisites: Linear Regression in Python
Before you build neural networks, you must master **linear regression**—the foundation of all supervised learning. This course teaches you the **math, code, and intuition** behind linear models, from simple one-variable fits to multivariate regression with real-world datasets.
What You’ll Build & Learn
- Linear regression from scratch—using only NumPy and calculus
- Gradient descent implementation—batch, stochastic, and mini-batch
- Model evaluation—R², MSE, MAE, and residual analysis
- Polynomial regression—model non-linear relationships
- Real-world projects—predict housing prices, exam scores, and sales
Why This Matters
Linear regression isn’t just a “basic” algorithm—it’s the **core intuition** behind loss functions, optimization, and generalization in deep learning. Understanding it deeply makes everything else easier.
Who Is This For?
- Beginners starting their machine learning journey
- Coders who’ve used sklearn but want to understand the math
- Students preparing for ML interviews
- Professionals filling foundational gaps
No Fluff—Just Core Understanding
This course skips hype and focuses on **first principles**. You’ll derive gradients by hand, debug convergence issues, and build models that generalize.
Ready to build rock-solid ML foundations? Enroll now.
