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.
Logistic Regression with NumPy and Python
Logistic Regression with NumPy and Python
ไฝๆๅธ๏ผSnehan Kekre
่ฎฟ้ฎๆ้็ฑ Coursera Learning Team ๆไพ
13,841 ไบบๅทฒๆณจๅ
๏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
- Classification Algorithms
- Machine Learning Algorithms
- Data Science
- Data Analysis
- Python Programming
- Seaborn
- Machine Learning
- Supervised Learning
- NumPy
- Matplotlib
- Logistic Regression
- Jupyter
- Data Visualization
- Algorithms
- ๆ่ฝ้จๅๅทฒๆๅ ใๆพ็คบ 12 ้กนๆ่ฝ๏ผๅ ฑ 14 ้กนใ
่ฆไบ่งฃ็่ฏฆ็ปไฟกๆฏ
ๆทปๅ ๅฐๆจ็้ข่ฑๆกฃๆก
ไป ๆก้ขๅฏ็จ
ไบ่งฃ้กถ็บงๅ ฌๅธ็ๅๅทฅๅฆไฝๆๆก็ญ้จๆ่ฝ
ๅจ 2 ๅฐๆถๅ ๅญฆไน ใ็ปไน ๅนถๅบ็จๅฒไฝๅฟ ๅคๆ่ฝ
- ๆฅๅ่กไธไธๅฎถ็ๅน่ฎญ
- ่ทๅพ่งฃๅณๅฎ่ฎญๅทฅไฝไปปๅก็ๅฎ่ทต็ป้ช
- ไฝฟ็จๆๆฐ็ๅทฅๅ ทๅๆๆฏๆฅๅปบ็ซไฟกๅฟ
ๅ ณไบๆญคๆๅฏผ้กน็ฎ
ๅๆญฅ่ฟ่กๅญฆไน
ๅจไธๆจ็ๅทฅไฝๅบไธ่ตทๅจๅๅฑไธญๆญๆพ็่ง้ขไธญ๏ผๆจ็ๆ่ฏพๆๅธๅฐๆๅฏผๆจๅฎๆๆฏไธชๆญฅ้ชค๏ผ
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Introduction and Project Overview
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Load the Data and Import Libraries
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Visualize the Data
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Define the Logistic Sigmoid Function ๐(๐ง)
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Compute the Cost Function ๐ฝ(๐) and Gradient
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Cost and Gradient at Initialization
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Implement Gradient Descent
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Plotting the Convergence of ๐ฝ(๐)
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Plotting the Decision Boundary
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Predictions Using the Optimized ๐ Values
ๆจ่ไฝ้ช
Prior programming experience in Python and machine learning theory is recommended.
7ไธช้กน็ฎๅพ็
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ๆไพๆน
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ไบบไปฌไธบไปไน้ๆฉ Coursera ๆฅๅธฎๅฉ่ชๅทฑๅฎ็ฐ่ไธๅๅฑ
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ๅญฆ็่ฏ่ฎบ
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64.89%
- 4 stars
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- 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 Jun 8, 2020ๅฎก้
I really enjoyed this course. Thank you for your valuable teaching.
ๅทฒไบ Jul 14, 2020ๅฎก้
Gain more understanding about LR and gradient descent practically.
