| 1. Course Structure.mp4 | 8.8 MB | ||
| 1. Housing data Implementation part 1.mp4 | 131.5 MB | ||
| 1. Introduction and Implementation.mp4 | 123.2 MB | ||
| 1. Introduction to Linear regression.mp4 | 52.8 MB | ||
| 1. Introduction to classification.mp4 | 28.6 MB | ||
| 1. Thank you.mp4 | 24 MB | ||
| 1.1 Advanced_Linear_and_Logistic_Regression_implementation.ipynb | 5 KB | ||
| 1.1 Introduction_to_classification.ipynb | 1.4 KB | ||
| 1.1 Linear_Regression.ipynb | 68.2 KB | ||
| 1.2 Diabetes_Project_with_logistic_Regression.ipynb | 14.4 KB | ||
| 2. Housing data Implementation Part 2.mp4 | 121.5 MB | ||
| 2. IMPORTANT NOTES PLEASE DO NOT SKIP.mp4 | 4.7 MB | ||
| 2. Introduction to Logistic Regression.mp4 | 38.3 MB | ||
| 2. Linear Regression Implementation.mp4 | 78.2 MB | ||
| 2.1 Advanced_Linear_and_Logistic_Regression_implementation (1).ipynb | 10.1 MB | ||
| 2.1 Introduction_to_Logistic_Regression.ipynb | 143.3 KB | ||
| 2.1 Linear_Regression (1).ipynb | 98.9 KB | ||
| 3. Housing data implementation Part 3.mp4 | 102.5 MB | ||
| 3. How to make out of this course.mp4 | 8.8 MB | ||
| 3. Introduction to multiple and multivariate linear regression.mp4 | 75.3 MB | ||
| 3. Logistic Regression implementation Part 1.mp4 | 75.4 MB | ||
| 3.1 Advanced_Linear_and_Logistic_Regression_implementation (2).ipynb | 20.3 MB | ||
| 3.1 Multiple_and_multivariate_Linear_Regression.ipynb | 122.8 KB | ||
| 4. Housing data implementation Part 4.mp4 | 102.2 MB | ||
| 4. Logistic Regression implementation Part 2.mp4 | 83.1 MB | ||
| 4. Simple linear regression using TensorFlow Keras.mp4 | 110.6 MB | ||
| 4. What is regression.mp4 | 57.7 MB | ||
| 4.1 Advanced_Linear_and_Logistic_Regression_implementation (3).ipynb | 20.2 MB | ||
| 4.1 Linear_Regression_Implementation_with_Tensorflow_Keras.ipynb | 44.8 KB | ||
| 4.1 Regression.ipynb | 112.4 KB | ||
| 5. Housing data implementation Part 5.mp4 | 107.3 MB | ||
| 5. Logistic Regression Implementation Final part.mp4 | 67.3 MB | ||
| 5. Multiple and multivariate linear regression Implementation Part 1.mp4 | 90.9 MB | ||
| 5.1 Advanced_Linear_and_Logistic_Regression_implementation (4).ipynb | 30.4 MB | ||
| 5.1 Introduction_to_Logistic_Regression (2).ipynb | 191.6 KB | ||
| 5.1 Multiple_and_multivariate_Linear_Regression (1).ipynb | 567.5 KB | ||
| 6. Housing data implementation Part 6.mp4 | 122 MB | ||
| 6. Multiple and multivariate linear regression Implementation Part 2.mp4 | 68.8 MB | ||
| 6.1 Advanced_Linear_and_Logistic_Regression_implementation (5).ipynb | 40.5 MB | ||
| 6.1 Multiple_and_multivariate_Linear_Regression (2).ipynb | 601.9 KB | ||
| 7. Housing data implementation Part 7.mp4 | 108 MB | ||
| 7. Multiple and multivariate linear regression Implementation Final Part.mp4 | 50.5 MB | ||
| 7.1 Advanced_Linear_and_Logistic_Regression_implementation (7).ipynb | 60.8 MB | ||
| 7.1 Multiple_and_multivariate_Linear_Regression (3).ipynb | 623.5 KB | ||
| 8. Housing data implementation Final Part.mp4 | 66.3 MB | ||
| 8.1 Advanced_Linear_and_Logistic_Regression_implementation (9).ipynb | 70.9 MB | ||
| 9. Breast Cancer project Implementation with Logistic Regression.mp4 | 140.5 MB | ||
| 9.1 Advanced_Linear_and_Logistic_Regression_implementation (10).ipynb | 81 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| diabetes.csv | 25.2 KB | ||
| ▲ 51 total files | |||
The Complete Linear and Logistic Regression Course in Python
https://DevCourseWeb.com
Published 1/2023
Created by Hoang Quy La
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 27 Lectures ( 4h 46m ) | Size: 2.32 GB
Lasso and Ridge Regression, Elastic Net Regression, Linear Regression, Logistic Regression, pickle, tempfile.
What you'll learn
Tensorflow
Tensorboard
pandas
ReLU activation function.
Seaborn
Google Colab
Import data from the UCI repository.
scikit-learn
Logistic Regression.
Linear Regression.
numpy
pickle
tempfile
Lasso and Ridge Regression
Elastic Net Regression
Multiple and multivariate linear regression
TensorFlow Keras API
Requirements
Basic knowledge of Python is required.
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