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[FreeCourseSite.com] Udemy - Machine Learning Regression Masterclass in Python
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[FreeCourseSite.com] Udemy - Machine Learning Regression Masterclass in Python
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e73355c1b6b571ce7da7607c9ea4e2fe428b4382
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收录时间:
2021-05-28
最近下载:
2024-12-10
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文件列表
4. REGRESSION KEY PERFORMANCE INDICATORS/4. Bias Variance Tradeoff.mp4
199.4 MB
6. MULTIPLE LINEAR REGRESSION/5. Project #1 - Model Training and Evaluation.mp4
165.2 MB
3. SIMPLE LINEAR REGRESSION/8. Project #1 - Test Model.mp4
150.8 MB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/4. ML vs. DL vs. AI.mp4
150.7 MB
9. LASSO AND RIDGE REGRESSION/5. Ridge and Lasso in Practice.mp4
148.9 MB
6. MULTIPLE LINEAR REGRESSION/6. Project #1 - Model Results Evaluation.mp4
142.3 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/12. Evaluate the Model.mp4
138.7 MB
6. MULTIPLE LINEAR REGRESSION/12. Project #2 - Retraining Model.mp4
135.6 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/9. Project - Visualize Dataset.mp4
135.5 MB
4. REGRESSION KEY PERFORMANCE INDICATORS/2. Regression Metric Part 1.mp4
134.6 MB
3. SIMPLE LINEAR REGRESSION/4. Project #1 - Overview.mp4
132.9 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/11. Train the Model.mp4
127.7 MB
6. MULTIPLE LINEAR REGRESSION/9. Project #2 - Data Visualization.mp4
123.3 MB
3. SIMPLE LINEAR REGRESSION/2. Simple Linear Regression Intuition.mp4
102.6 MB
7. LOGISTIC REGRESSION/7. Project #2 - Training Testing.mp4
97.9 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/5. Theory Part 4.mp4
95.0 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/14. Model Improvement with more features.mp4
95.0 MB
9. LASSO AND RIDGE REGRESSION/2. Ridge Lasso Part 1.mp4
92.8 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/8. Project - Load Dataset.mp4
91.0 MB
7. LOGISTIC REGRESSION/8. Model Testing Visualization.mp4
89.9 MB
5. POLYNOMIAL REGRESSION/5. Poly Regression - Linear Trainingtesting.mp4
83.1 MB
6. MULTIPLE LINEAR REGRESSION/11. Project #2 - Model Evaluation.mp4
82.5 MB
3. SIMPLE LINEAR REGRESSION/14. Project #2 - Model Testing.mp4
82.3 MB
6. MULTIPLE LINEAR REGRESSION/4. Project #1 - Data Visualization.mp4
81.4 MB
7. LOGISTIC REGRESSION/5. Project #2 - Visualization.mp4
80.5 MB
4. REGRESSION KEY PERFORMANCE INDICATORS/3. Regression Metric Part 2.mp4
80.2 MB
5. POLYNOMIAL REGRESSION/3. Poly Regression - Salary Load Data.mp4
77.5 MB
3. SIMPLE LINEAR REGRESSION/6. Project #1 - Divide Data into Training and Testing.mp4
77.4 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/13. Multiple Linear regression.mp4
76.0 MB
3. SIMPLE LINEAR REGRESSION/5. Project #1 - Data Visualization.mp4
74.9 MB
7. LOGISTIC REGRESSION/3. Confusion Matrix.mp4
74.0 MB
6. MULTIPLE LINEAR REGRESSION/8. Project #2 - Load Data.mp4
73.1 MB
9. LASSO AND RIDGE REGRESSION/3. Ridge Lasso Part 2.mp4
71.6 MB
5. POLYNOMIAL REGRESSION/2. Polynomial Regression - Intuition.mp4
70.4 MB
7. LOGISTIC REGRESSION/2. Logistic Regression Intuition.mp4
67.2 MB
5. POLYNOMIAL REGRESSION/4. Poly Regression - Visualize Data.mp4
66.7 MB
5. POLYNOMIAL REGRESSION/11. Poly Regression - Economies Poly.mp4
65.7 MB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/1. Course Welcome Message.mp4
63.4 MB
3. SIMPLE LINEAR REGRESSION/3. Least Squares.mp4
62.7 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/10. Scale the Data.mp4
62.0 MB
6. MULTIPLE LINEAR REGRESSION/3. Project #1 - Load Data and Libraries.mp4
59.6 MB
5. POLYNOMIAL REGRESSION/10. Poly Regression - Economies Linear -2.mp4
59.1 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/4. Theory Part 3.mp4
58.5 MB
5. POLYNOMIAL REGRESSION/6. Poly Regression - Poly Part 1.mp4
57.2 MB
6. MULTIPLE LINEAR REGRESSION/10. Project #2 - Train the Model.mp4
55.7 MB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/3. Course Overview.mp4
55.6 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/6. Theory Part 5.mp4
52.8 MB
3. SIMPLE LINEAR REGRESSION/7. Project #1 - Train Model.mp4
52.7 MB
5. POLYNOMIAL REGRESSION/9. Poly Regression - Economies Linear -1.mp4
51.2 MB
3. SIMPLE LINEAR REGRESSION/11. Project #2 - Visualization.mp4
50.6 MB
2. ANACONDA AND JUPYTER INSTALLATION/1. Download and Set up Anaconda.mp4
46.9 MB
7. LOGISTIC REGRESSION/6. Project #2 - Data Cleaning.mp4
45.8 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/2. Theory Part 1.mp4
41.9 MB
7. LOGISTIC REGRESSION/4. Project #2 - Data Import.mp4
38.8 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/7. Theory Part 6.mp4
37.6 MB
5. POLYNOMIAL REGRESSION/7. Poly Regression - Poly Part 2.mp4
37.4 MB
3. SIMPLE LINEAR REGRESSION/10. Project #2 - Solution.mp4
35.7 MB
9. LASSO AND RIDGE REGRESSION/4. Ridge Lasso Part 3.mp4
34.7 MB
5. POLYNOMIAL REGRESSION/8. Poly Regression Project 2 Overview.mp4
34.0 MB
2. ANACONDA AND JUPYTER INSTALLATION/2. What is Jupiter Notebook.mp4
34.0 MB
3. SIMPLE LINEAR REGRESSION/12. Project #2 - Prepare Training and Testing Data.mp4
33.5 MB
3. SIMPLE LINEAR REGRESSION/13. Project #2 - Test Model.mp4
33.1 MB
7. LOGISTIC REGRESSION/1. Logistic Regression Intro.mp4
31.9 MB
3. SIMPLE LINEAR REGRESSION/9. Project #2 - Overview.mp4
31.5 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/3. Theory Part 2.mp4
29.3 MB
3. SIMPLE LINEAR REGRESSION/1. Intro to Simple Linear Regression.mp4
28.5 MB
6. MULTIPLE LINEAR REGRESSION/7. Project #2 - Overview.mp4
27.3 MB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/3.1 ML Regression Course Package.zip.zip
26.6 MB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/2. Updates on Udemy Reviews.mp4
24.2 MB
9. LASSO AND RIDGE REGRESSION/1. Ridge and Lasso Intro.mp4
23.6 MB
6. MULTIPLE LINEAR REGRESSION/2. Multiple Linear Regression Overview.mp4
22.4 MB
6. MULTIPLE LINEAR REGRESSION/1. Multiple Linear Regression Intro.mp4
20.8 MB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/1. Artificial Neural Networks Intro.mp4
17.0 MB
5. POLYNOMIAL REGRESSION/1. Polynomial Regression Intro.mp4
15.5 MB
4. REGRESSION KEY PERFORMANCE INDICATORS/1. Regression Metrics Intro.mp4
13.7 MB
4. REGRESSION KEY PERFORMANCE INDICATORS/4. Bias Variance Tradeoff.vtt
29.6 kB
6. MULTIPLE LINEAR REGRESSION/5. Project #1 - Model Training and Evaluation.vtt
25.8 kB
3. SIMPLE LINEAR REGRESSION/8. Project #1 - Test Model.vtt
25.0 kB
3. SIMPLE LINEAR REGRESSION/4. Project #1 - Overview.vtt
24.9 kB
4. REGRESSION KEY PERFORMANCE INDICATORS/2. Regression Metric Part 1.vtt
22.0 kB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/4. ML vs. DL vs. AI.vtt
21.9 kB
6. MULTIPLE LINEAR REGRESSION/6. Project #1 - Model Results Evaluation.vtt
21.1 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/12. Evaluate the Model.vtt
20.2 kB
9. LASSO AND RIDGE REGRESSION/5. Ridge and Lasso in Practice.vtt
18.8 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/11. Train the Model.vtt
18.6 kB
6. MULTIPLE LINEAR REGRESSION/12. Project #2 - Retraining Model.vtt
17.1 kB
6. MULTIPLE LINEAR REGRESSION/9. Project #2 - Data Visualization.vtt
16.8 kB
3. SIMPLE LINEAR REGRESSION/2. Simple Linear Regression Intuition.vtt
16.8 kB
7. LOGISTIC REGRESSION/3. Confusion Matrix.vtt
16.2 kB
7. LOGISTIC REGRESSION/7. Project #2 - Training Testing.vtt
16.0 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/9. Project - Visualize Dataset.vtt
15.9 kB
3. SIMPLE LINEAR REGRESSION/5. Project #1 - Data Visualization.vtt
14.3 kB
3. SIMPLE LINEAR REGRESSION/6. Project #1 - Divide Data into Training and Testing.vtt
14.3 kB
5. POLYNOMIAL REGRESSION/5. Poly Regression - Linear Trainingtesting.vtt
14.2 kB
7. LOGISTIC REGRESSION/5. Project #2 - Visualization.vtt
14.0 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/5. Theory Part 4.vtt
13.7 kB
3. SIMPLE LINEAR REGRESSION/14. Project #2 - Model Testing.vtt
13.6 kB
5. POLYNOMIAL REGRESSION/3. Poly Regression - Salary Load Data.vtt
13.4 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/8. Project - Load Dataset.vtt
13.4 kB
5. POLYNOMIAL REGRESSION/2. Polynomial Regression - Intuition.vtt
13.3 kB
7. LOGISTIC REGRESSION/8. Model Testing Visualization.vtt
13.2 kB
6. MULTIPLE LINEAR REGRESSION/4. Project #1 - Data Visualization.vtt
12.9 kB
9. LASSO AND RIDGE REGRESSION/2. Ridge Lasso Part 1.vtt
12.8 kB
6. MULTIPLE LINEAR REGRESSION/11. Project #2 - Model Evaluation.vtt
12.7 kB
4. REGRESSION KEY PERFORMANCE INDICATORS/3. Regression Metric Part 2.vtt
12.7 kB
5. POLYNOMIAL REGRESSION/4. Poly Regression - Visualize Data.vtt
12.3 kB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/3. Course Overview.vtt
12.2 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/14. Model Improvement with more features.vtt
12.1 kB
7. LOGISTIC REGRESSION/2. Logistic Regression Intuition.vtt
10.9 kB
5. POLYNOMIAL REGRESSION/11. Poly Regression - Economies Poly.vtt
10.5 kB
9. LASSO AND RIDGE REGRESSION/3. Ridge Lasso Part 2.vtt
10.2 kB
3. SIMPLE LINEAR REGRESSION/7. Project #1 - Train Model.vtt
10.1 kB
6. MULTIPLE LINEAR REGRESSION/8. Project #2 - Load Data.vtt
10.1 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/13. Multiple Linear regression.vtt
10.0 kB
6. MULTIPLE LINEAR REGRESSION/10. Project #2 - Train the Model.vtt
10.0 kB
5. POLYNOMIAL REGRESSION/10. Poly Regression - Economies Linear -2.vtt
10.0 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/4. Theory Part 3.vtt
9.8 kB
3. SIMPLE LINEAR REGRESSION/3. Least Squares.vtt
9.6 kB
5. POLYNOMIAL REGRESSION/6. Poly Regression - Poly Part 1.vtt
9.6 kB
6. MULTIPLE LINEAR REGRESSION/3. Project #1 - Load Data and Libraries.vtt
9.6 kB
3. SIMPLE LINEAR REGRESSION/11. Project #2 - Visualization.vtt
9.5 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/6. Theory Part 5.vtt
9.1 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/10. Scale the Data.vtt
8.9 kB
5. POLYNOMIAL REGRESSION/9. Poly Regression - Economies Linear -1.vtt
7.8 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/2. Theory Part 1.vtt
7.5 kB
7. LOGISTIC REGRESSION/6. Project #2 - Data Cleaning.vtt
7.4 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/7. Theory Part 6.vtt
7.1 kB
5. POLYNOMIAL REGRESSION/7. Poly Regression - Poly Part 2.vtt
6.3 kB
7. LOGISTIC REGRESSION/4. Project #2 - Data Import.vtt
6.3 kB
3. SIMPLE LINEAR REGRESSION/12. Project #2 - Prepare Training and Testing Data.vtt
6.0 kB
3. SIMPLE LINEAR REGRESSION/13. Project #2 - Test Model.vtt
5.9 kB
2. ANACONDA AND JUPYTER INSTALLATION/1. Download and Set up Anaconda.vtt
5.8 kB
3. SIMPLE LINEAR REGRESSION/10. Project #2 - Solution.vtt
5.5 kB
9. LASSO AND RIDGE REGRESSION/4. Ridge Lasso Part 3.vtt
5.3 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/3. Theory Part 2.vtt
5.1 kB
5. POLYNOMIAL REGRESSION/8. Poly Regression Project 2 Overview.vtt
5.0 kB
2. ANACONDA AND JUPYTER INSTALLATION/2. What is Jupiter Notebook.vtt
4.7 kB
3. SIMPLE LINEAR REGRESSION/9. Project #2 - Overview.vtt
4.4 kB
6. MULTIPLE LINEAR REGRESSION/7. Project #2 - Overview.vtt
4.3 kB
6. MULTIPLE LINEAR REGRESSION/2. Multiple Linear Regression Overview.vtt
4.1 kB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/1. Course Welcome Message.vtt
3.5 kB
7. LOGISTIC REGRESSION/1. Logistic Regression Intro.vtt
1.8 kB
3. SIMPLE LINEAR REGRESSION/1. Intro to Simple Linear Regression.vtt
1.7 kB
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/2. Updates on Udemy Reviews.vtt
1.5 kB
6. MULTIPLE LINEAR REGRESSION/1. Multiple Linear Regression Intro.vtt
1.2 kB
9. LASSO AND RIDGE REGRESSION/1. Ridge and Lasso Intro.vtt
1.2 kB
10. Bonus Lectures/1. YOUR SPECIAL BONUS.html
1.1 kB
8. APPLY ARTIFICIAL NEURAL NETWORKS TO PERFROM REGRESSION TASKS/1. Artificial Neural Networks Intro.vtt
962 Bytes
5. POLYNOMIAL REGRESSION/1. Polynomial Regression Intro.vtt
922 Bytes
4. REGRESSION KEY PERFORMANCE INDICATORS/1. Regression Metrics Intro.vtt
815 Bytes
0. Websites you may like/[FCS Forum].url
133 Bytes
0. Websites you may like/[FreeCourseSite.com].url
127 Bytes
0. Websites you may like/[CourseClub.ME].url
122 Bytes
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