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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
Readme.txt 140 Bytes
1. INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]/[Tutorialsplanet.NET].url 128 Bytes
10. Bonus Lectures/[Tutorialsplanet.NET].url 128 Bytes
5. POLYNOMIAL REGRESSION/[Tutorialsplanet.NET].url 128 Bytes
7. LOGISTIC REGRESSION/[Tutorialsplanet.NET].url 128 Bytes
[Tutorialsplanet.NET].url 128 Bytes

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[Tutorialsplanet.NET] Udemy - Machine Learning Regression Masterclass in Python

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