[DesireCourse.Net] Udemy - Beginning with Machine Learning & Data Science in Python

Creation Time	Dec. 25, 2023, 9:44 p.m.
Last Access Time	Sept. 21, 2024, 2:06 p.m.
File Size	542.7 MB
Keywords	Beginning Data Python DesireCourse in - Udemy Machine Learning Science Net with
Total Requests	4
Total Files	121
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2. Understanding Data Wrangling/7. Cleaning Messy Data.mp4 32.0 MB
2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.mp4 25.1 MB
6. Regularization/6. Regularization using scikit-learn.mp4 22.9 MB
3. Linear Regression/5. Simple Linear Regression.mp4 21.9 MB
2. Understanding Data Wrangling/6. Which month was the snowiest.mp4 20.4 MB
3. Linear Regression/11. Handling categorical features.mp4 19.8 MB
2. Understanding Data Wrangling/4. Which borough has the most noise complaints.mp4 19.5 MB
1. Working with Machine Learning/5. Python and Jupyter Demo.mp4 17.7 MB
2. Understanding Data Wrangling/5. Which weekday do people bike the most.mp4 17.0 MB
6. Regularization/4. Regularizing linear models.mp4 16.9 MB
2. Understanding Data Wrangling/8. How to deal with timestamps.mp4 16.4 MB
4. Logistic Regression/8. Interpreting logistic regression.mp4 16.3 MB
2. Understanding Data Wrangling/2. Reading from a CSV.mp4 16.1 MB
5. Cross Validation/4. Cross-validation continued.mp4 15.9 MB
4. Logistic Regression/4. Predicting a categorical response.mp4 15.7 MB
3. Linear Regression/8. Multiple linear regression.mp4 15.3 MB
4. Logistic Regression/6. Probability, odds, log-odds.mp4 15.1 MB
2. Understanding Data Wrangling/9. Loading data from SQL databases.mp4 13.4 MB
6. Regularization/8. Pipeline and GridSearchCV.mp4 12.6 MB
6. Regularization/3. Overfitting with linear models.mp4 12.5 MB
4. Logistic Regression/2. Predicting a continuous response.mp4 11.6 MB
4. Logistic Regression/5. Using logistic regression.mp4 11.4 MB
6. Regularization/7. Regularizing logistic models.mp4 11.2 MB
4. Logistic Regression/7. What is logistic regression.mp4 10.9 MB
3. Linear Regression/10. Model evaluation.mp4 10.7 MB
6. Regularization/5. Ridge and Lasso Regularization.mp4 8.9 MB
1. Working with Machine Learning/3. Install Anaconda.mp4 8.8 MB
2. Understanding Data Wrangling/2.2 311-service-requests.zip.zip 8.3 MB
5. Cross Validation/3. K-fold cross-validation.mp4 8.0 MB
3. Linear Regression/6. Hypothesis testing and p-values.mp4 7.8 MB
5. Cross Validation/2. Traintest split.mp4 7.4 MB
1. Working with Machine Learning/1. Exploring Machine Learning and its Types.mp4 7.3 MB
4. Logistic Regression/9. Using logistic regression with categorical features.mp4 7.3 MB
3. Linear Regression/9. Model and feature selection.mp4 7.1 MB
3. Linear Regression/3. The advertising dataset.mp4 7.1 MB
3. Linear Regression/7. R squared.mp4 5.8 MB
3. Linear Regression/12. Summary.mp4 5.5 MB
4. Logistic Regression/3. Quick refresher on linear regression.mp4 4.9 MB
5. Cross Validation/5. Summary.mp4 4.9 MB
3. Linear Regression/4. EDA questions on advertising data.mp4 4.7 MB
6. Regularization/2. Overfitting.mp4 4.7 MB
6. Regularization/9. Comparing regularized with unregularized models.mp4 3.2 MB
3. Linear Regression/2. What is linear regression.mp4 2.8 MB
3. Linear Regression/1. Introduction.mp4 1.7 MB
4. Logistic Regression/2.1 logistic regression.zip.zip 1.3 MB
6. Regularization/1. Introduction.mp4 1.2 MB
4. Logistic Regression/10. Summary.mp4 896.8 KB
5. Cross Validation/1. Introduction.mp4 891.7 KB
4. Logistic Regression/1. Introduction.mp4 891.3 KB
2. Understanding Data Wrangling/10. Summary.mp4 539.4 KB
2. Understanding Data Wrangling/1. Introduction.mp4 498.6 KB
2. Understanding Data Wrangling/9.2 weather_2012.csv.csv 492.0 KB
2. Understanding Data Wrangling/2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip 395.7 KB
6. Regularization/2.1 regularization.zip.zip 366.7 KB
2. Understanding Data Wrangling/8.2 popularity-contest.tsv.tsv 185.2 KB
3. Linear Regression/3.1 linear regression.zip.zip 176.2 KB
1. Working with Machine Learning/5.1 A quick tour of IPython Notebook.zip.zip 102.8 KB
2. Understanding Data Wrangling/6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip 78.4 KB
2. Understanding Data Wrangling/5.1 Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb.zip.zip 77.8 KB
2. Understanding Data Wrangling/3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip 38.8 KB
5. Cross Validation/2.1 cross validation.zip.zip 23.8 KB
2. Understanding Data Wrangling/4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip 18.1 KB
2. Understanding Data Wrangling/5.2 bikes.csv.csv 13.5 KB
2. Understanding Data Wrangling/7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip 11.2 KB
3. Linear Regression/5. Simple Linear Regression.vtt 9.8 KB
2. Understanding Data Wrangling/7. Cleaning Messy Data.vtt 9.4 KB
1. Working with Machine Learning/5. Python and Jupyter Demo.vtt 9.2 KB
3. Linear Regression/11. Handling categorical features.vtt 8.5 KB
2. Understanding Data Wrangling/9. Loading data from SQL databases.vtt 7.4 KB
5. Cross Validation/4. Cross-validation continued.vtt 7.0 KB
6. Regularization/4. Regularizing linear models.vtt 7.0 KB
2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.vtt 6.6 KB
2. Understanding Data Wrangling/6. Which month was the snowiest.vtt 6.6 KB
4. Logistic Regression/8. Interpreting logistic regression.vtt 6.3 KB
2. Understanding Data Wrangling/4. Which borough has the most noise complaints.vtt 6.2 KB
6. Regularization/3. Overfitting with linear models.vtt 6.1 KB
2. Understanding Data Wrangling/2. Reading from a CSV.vtt 5.9 KB
1. Working with Machine Learning/1. Exploring Machine Learning and its Types.vtt 5.8 KB
4. Logistic Regression/4. Predicting a categorical response.vtt 5.8 KB
2. Understanding Data Wrangling/5. Which weekday do people bike the most.vtt 5.7 KB
6. Regularization/6. Regularization using scikit-learn.vtt 5.6 KB
1. Working with Machine Learning/3. Install Anaconda.vtt 5.6 KB
4. Logistic Regression/6. Probability, odds, log-odds.vtt 5.5 KB
3. Linear Regression/8. Multiple linear regression.vtt 5.2 KB
3. Linear Regression/10. Model evaluation.vtt 4.8 KB
4. Logistic Regression/7. What is logistic regression.vtt 4.8 KB
2. Understanding Data Wrangling/8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip 4.4 KB
2. Understanding Data Wrangling/8. How to deal with timestamps.vtt 4.4 KB
2. Understanding Data Wrangling/9.1 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip 4.2 KB
4. Logistic Regression/2. Predicting a continuous response.vtt 4.1 KB
6. Regularization/8. Pipeline and GridSearchCV.vtt 3.9 KB
4. Logistic Regression/5. Using logistic regression.vtt 3.9 KB
5. Cross Validation/3. K-fold cross-validation.vtt 3.7 KB
5. Cross Validation/2. Traintest split.vtt 3.6 KB
6. Regularization/5. Ridge and Lasso Regularization.vtt 3.4 KB
3. Linear Regression/9. Model and feature selection.vtt 3.3 KB
3. Linear Regression/3. The advertising dataset.vtt 3.1 KB
3. Linear Regression/6. Hypothesis testing and p-values.vtt 2.9 KB
3. Linear Regression/12. Summary.vtt 2.8 KB
4. Logistic Regression/9. Using logistic regression with categorical features.vtt 2.7 KB
3. Linear Regression/7. R squared.vtt 2.6 KB
6. Regularization/2. Overfitting.vtt 2.4 KB
6. Regularization/7. Regularizing logistic models.vtt 2.1 KB
5. Cross Validation/5. Summary.vtt 2.1 KB
6. Regularization/9. Comparing regularized with unregularized models.vtt 1.8 KB
3. Linear Regression/4. EDA questions on advertising data.vtt 1.8 KB
3. Linear Regression/2. What is linear regression.vtt 1.7 KB
2. Understanding Data Wrangling/9.3 weather_2012_sqlite.zip.zip 1.4 KB
4. Logistic Regression/3. Quick refresher on linear regression.vtt 1.3 KB
3. Linear Regression/1. Introduction.vtt 1.2 KB
6. Regularization/1. Introduction.vtt 668 bytes
4. Logistic Regression/1. Introduction.vtt 469 bytes
5. Cross Validation/1. Introduction.vtt 457 bytes
2. Understanding Data Wrangling/10. Summary.vtt 396 bytes
4. Logistic Regression/10. Summary.vtt 371 bytes
2. Understanding Data Wrangling/1. Introduction.vtt 281 bytes
1. Working with Machine Learning/2. Machine Learning Foundations.html 159 bytes
1. Working with Machine Learning/4. Python Versions.html 159 bytes
1. Working with Machine Learning/6. Python Basics.html 159 bytes
[DesireCourse.Net].url 51 bytes
[CourseClub.Me].url 48 bytes

[DesireCourse.Net] Udemy - Beginning with Machine Learning & Data Science in Python

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