Implementation Lineare Regression & Gradient Descent, Machine Learning Tutorial #8
In this video, I'll show you an implementation of linear regression and the gradient descent method. I'll use a JupyterLab environment to present two different examples in Python. In the first example, I'll use simple, self-generated data to show you how to implement linear regression and the gradient descent method yourself. In the second example, I'll turn to a real dataset and briefly discuss data analysis. I'll then use a Python library to apply and evaluate the linear regression model to this real data.
Timestamps:
0:00 Intro
1:07 Start of Example 1
2:06 Step 1: Collecting Data (Ex. 1)
4:26 Step 2: Preparing Data (Ex. 1)
5:22 Step 3: Training the Model (Ex. 1)
10:46 Step 4: Evaluating the Model (Ex. 1)
11:28 Step 5: Parameter Tuning (Ex. 1)
11:53 Step 6: Test Data (Ex. 1)
12:24 Start of Example 2
12:50 Step 1: Collecting Data (Ex. 2)
14:20 Step 2: Preparing Data (Ex. 2)
21:07 Step 3: Training the Model (Ex. 2)
21:57 Step 4: Evaluating the Model (Ex. 2)
23:45 Step 5: Parameter Tuning (Ex. 2)
24:03 Step 6: Test Data (Example 2)
24:26 Outro
Sources:
Intro Image: Ahmed Gad on Pixabay (with slight color changes)
JupyterLab: https://jupyter.org/
Kaggle data: https://www.kaggle.com/datasets/nehal...
Github Code: https://github.com/codingwithmagga/ma...
My website:
https://codingwithmagga.com/
Patreon:
/ codingwithmagga
PayPal:
https://www.paypal.com/paypalme/codin...
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