Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
Unlock access to 10,000+ courses with Coursera Plus. Start 7-Day free trial.
๏ผ396 ๆก่ฏ่ฎบ๏ผ
ๆจ่ไฝ้ช
ๆจๅฐๅญฆๅฐไปไน
Implement the gradient descent algorithm from scratch
Perform logistic regression with NumPy and Python
Create data visualizations with Matplotlib and Seaborn
ๆจๅฐ็ปไน ็ๆ่ฝ
่ฆไบ่งฃ็่ฏฆ็ปไฟกๆฏ
ๆทปๅ ๅฐๆจ็้ข่ฑๆกฃๆก
ไป ๆก้ขๅฏ็จ
ไบ่งฃ้กถ็บงๅ ฌๅธ็ๅๅทฅๅฆไฝๆๆก็ญ้จๆ่ฝ
ๅจ 2 ๅฐๆถๅ ๅญฆไน ใ็ปไน ๅนถๅบ็จๅฒไฝๅฟ ๅคๆ่ฝ
- ๆฅๅ่กไธไธๅฎถ็ๅน่ฎญ
- ่ทๅพ่งฃๅณๅฎ่ฎญๅทฅไฝไปปๅก็ๅฎ่ทต็ป้ช
- ไฝฟ็จๆๆฐ็ๅทฅๅ ทๅๆๆฏๆฅๅปบ็ซไฟกๅฟ
ๅ ณไบๆญคๆๅฏผ้กน็ฎ
ๅๆญฅ่ฟ่กๅญฆไน
ๅจไธๆจ็ๅทฅไฝๅบไธ่ตทๅจๅๅฑไธญๆญๆพ็่ง้ขไธญ๏ผๆจ็ๆ่ฏพๆๅธๅฐๆๅฏผๆจๅฎๆๆฏไธชๆญฅ้ชค๏ผ
Introduction and Project Overview
Load the Data and Import Libraries
Visualize the Data
Define the Logistic Sigmoid Function ๐(๐ง)
Compute the Cost Function ๐ฝ(๐) and Gradient
Cost and Gradient at Initialization
Implement Gradient Descent
Plotting the Convergence of ๐ฝ(๐)
Plotting the Decision Boundary
Predictions Using the Optimized ๐ Values
ๆจ่ไฝ้ช
Prior programming experience in Python and machine learning theory is recommended.
7ไธช้กน็ฎๅพ็
ไฝๆๅธ
ๆไพๆน
ๅญฆไน ๆนๅผ
ๅบไบๆ่ฝ็ๅฎ่ทตๅญฆไน
้่ฟๅฎๆไธๅทฅไฝ็ธๅ ณ็ไปปๅกๆฅ็ปไน ๆฐๆ่ฝใ
ไธๅฎถๆๅฏผ
ไฝฟ็จ็ฌ็น็ๅนถๆ็้ข๏ผๆ็ ง้ขๅ ๅฝๅถ็ไธๅฎถ่ง้ขๆไฝใ
ๆ ้ไธ่ฝฝๆๅฎ่ฃ
ๅจ้ข้ ็ฝฎ็ไบๅทฅไฝ็ฉบ้ดไธญ่ฎฟ้ฎๆ้็ๅทฅๅ ทๅ่ตๆบใ
ไป ๅจๅฐๅผ่ฎก็ฎๆบไธๅฏ็จ
ๆญคๆๅฏผ้กน็ฎไธไธบๅ ทๆๅฏ้ ไบ่็ฝ่ฟๆฅ็็ฌ่ฎฐๆฌ็ต่ๆๅฐๅผ่ฎก็ฎๆบ่่ฎพ่ฎก๏ผ่ไธๆฏ็งปๅจ่ฎพๅคใ
ไบบไปฌไธบไปไน้ๆฉ Coursera ๆฅๅธฎๅฉ่ชๅทฑๅฎ็ฐ่ไธๅๅฑ
ๅญฆ็่ฏ่ฎบ
396 ๆก่ฏ่ฎบ
- 5 stars
64.89%
- 4 stars
27.27%
- 3 stars
5.05%
- 2 stars
0.75%
- 1 star
2.02%
ๆพ็คบ 3/396 ไธช
ๅทฒไบ Aug 29, 2020ๅฎก้
Very helpful for learning logistic regression without using any libraries. Before taking this project one should have a clear understanding of Logistic Regression, then it will be very helpful
ๅทฒไบ Jul 14, 2020ๅฎก้
Gain more understanding about LR and gradient descent practically.
ๅทฒไบ Nov 7, 2021ๅฎก้
Wโell explained all the basic components of gradient descent. Exactly as advertised.
ๆจๅฏ่ฝ่ฟๅๆฌข
็ถๆ๏ผๅ ่ดน่ฏ็จ
Coursera
็ถๆ๏ผ้ข่ง
้่ฟ Coursera Plus ๅผๅฏๆฐ็ๆถฏ
ๆ ้ๅถ่ฎฟ้ฎ 10,000+ ไธ็ไธๆต็่ฏพ็จใๅฎ่ทต้กน็ฎๅๅฐฑไธๅฐฑ็ปช่ฏไนฆ่ฏพ็จ - ๆๆ่ฟไบ้ฝๅ ๅซๅจๆจ็่ฎข้ ไธญ
้่ฟๅจ็บฟๅญฆไฝๆจๅจๆจ็่ไธ็ๆถฏ
่ทๅไธ็ไธๆตๅคงๅญฆ็ๅญฆไฝ - 100% ๅจ็บฟ
ๅ ๅ ฅ่ถ ่ฟ 3400 ๅฎถ้ๆฉ Coursera for Business ็ๅ จ็ๅ ฌๅธ
ๆๅๅๅทฅ็ๆ่ฝ๏ผไฝฟๅ ถๅจๆฐๅญ็ปๆตไธญ่ฑ้ข่ๅบ
ๅธธ่ง้ฎ้ข
่ดญไนฐๆๅฏผ้กน็ฎๅ๏ผๆจๅฐ่ทๅพๅฎๆๆๅฏผ้กน็ฎๆ้็ไธๅ๏ผๅ ๆฌ้่ฟ Web ๆต่งๅจ่ฎฟ้ฎไบๆก้ขๅทฅไฝ็ฉบ้ด๏ผๅทฅไฝ็ฉบ้ดไธญๅ ๅซๆจ้่ฆไบ่งฃ็ๆไปถๅ่ฝฏไปถ๏ผไปฅๅ็นๅฎ้ขๅ็ไธๅฎถๆไพ็ๅๆญฅ่ง้ข่ฏดๆใ
็ฑไบๆจ็ๅทฅไฝ็ฉบ้ดๅ ๅซ้ๅ็ฌ่ฎฐๆฌ็ต่ๆๅฐๅผ่ฎก็ฎๆบไฝฟ็จ็ไบๆก้ข๏ผๅ ๆญคๆๅฏผ้กน็ฎไธๅจ็งปๅจ่ฎพๅคไธๆไพใ
ๆๅฏผ้กน็ฎๆ่ฏพๆๅธๆฏ็นๅฎ้ขๅ็ไธๅฎถ๏ผไปไปฌๅจ้กน็ฎ็ๆ่ฝใๅทฅๅ ทๆ้ขๅๆน้ข็ป้ชไธฐๅฏ๏ผๅนถไธ็ญ่กทไบๅไบซ่ชๅทฑ็็ฅ่ฏไปฅๅฝฑๅๅ จ็ๆฐ็พไธ็ๅญฆ็ใ