Supervised Machine Learning Model from Scratch | Polynomial Regression | Gradient Descent in Python

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Build a Supervised Learning Model from Scratch | Polynomial Regression with Gradient Descent in Python

In this video, we bring machine learning to life—from first principles to prediction—by building a supervised learning model from scratch using Python and a Jupyter Notebook.

You'll learn how to:

✅ Understand polynomial regression intuitively
✅ Implement gradient descent step by step
✅ Visualize predictions using Plotly
✅ Track model loss and improvement over time
✅ Make predictions on unseen data

No scikit-learn. No shortcuts. Just hands-on learning to help you master the core ideas!

📍 Chapters:
00:00 – Intro
00:30 – Data Setup & Polynomial Regression
01:45 – Model & Loss Function
02:05 – Gradient Descent Explained
02:25 – Hyperparameters (Learning Rate, Epochs, Training Loop)
04:55 – A surprise for my viewers
05:00 – Training the Model from Scratch in Python
12:48 – Making a Prediction
12:50 – Visualizing the Result

📚 Related Videos:

🛠 Tools Used:
Python
NumPy
Plotly
Jupyter Notebook inside a Dev Container (VS Code)

👍 If this helped, like the video, subscribe, and share it with your friends and family!
💬 Drop a comment if you have questions or want more content like this.

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#SupervisedLearning
#PolynomialRegression
#GradientDescent
#PythonCoding
#FromScratch
#JupyterNotebook
#DataScience
#MLTutorial
#AIForBeginners
#LearnPython
#CodingWithPython
#ArtificialIntelligence
#PythonProjects
#Plotly
#VSCode
#DevContainers
#TrainYourModel
#NoScikitLearn
#MLFromScratch

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