This video demonstrates how to implement Linear Regression using NumPy, covering both the Normal Equation and Gradient Descent, along with vectorization for efficient computation. Learn how to build and optimize models from scratch.
Topics Covered:
1. Implement Linear Regression from Scratch using NumPy
2. Normal Equation – Formula Breakdown & Implementation
3. Fit Method – Training the Model
4. Predict Method – Model Inference
5. Gradient Descent Implementation
6. Vectorization for Faster Computation
Helpful For:
1. Cracking AI / ML / Data Science interview rounds at top tech companies
2. Building a deeper understanding of core AI, ML concepts
3. Preparing for GATE (DA / CS / Other streams) and other related competitive exams
Code Link : https://drive.google.com/drive/folders/12Q3gjwbZG302uPuhwo45VhKsLq4HyB7f?usp=sharing
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Tags:
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