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

磁力链接/BT种子名称

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

磁力链接/BT种子简介

种子哈希：c4db89131dae7b02aeff820353dd60dd92cade21
文件大小：542.73M
已经下载：785次
下载速度：极快
收录时间：2021-03-06
最近下载：2024-09-21

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文件列表

  • 2. Understanding Data Wrangling/7. Cleaning Messy Data.mp4 33.6 MB
  • 2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.mp4 26.3 MB
  • 6. Regularization/6. Regularization using scikit-learn.mp4 24.0 MB
  • 3. Linear Regression/5. Simple Linear Regression.mp4 23.0 MB
  • 2. Understanding Data Wrangling/6. Which month was the snowiest.mp4 21.4 MB
  • 3. Linear Regression/11. Handling categorical features.mp4 20.8 MB
  • 2. Understanding Data Wrangling/4. Which borough has the most noise complaints.mp4 20.4 MB
  • 1. Working with Machine Learning/5. Python and Jupyter Demo.mp4 18.5 MB
  • 2. Understanding Data Wrangling/5. Which weekday do people bike the most.mp4 17.8 MB
  • 6. Regularization/4. Regularizing linear models.mp4 17.7 MB
  • 2. Understanding Data Wrangling/8. How to deal with timestamps.mp4 17.2 MB
  • 4. Logistic Regression/8. Interpreting logistic regression.mp4 17.1 MB
  • 2. Understanding Data Wrangling/2. Reading from a CSV.mp4 16.8 MB
  • 5. Cross Validation/4. Cross-validation continued.mp4 16.7 MB
  • 4. Logistic Regression/4. Predicting a categorical response.mp4 16.5 MB
  • 3. Linear Regression/8. Multiple linear regression.mp4 16.1 MB
  • 4. Logistic Regression/6. Probability, odds, log-odds.mp4 15.8 MB
  • 2. Understanding Data Wrangling/9. Loading data from SQL databases.mp4 14.1 MB
  • 6. Regularization/8. Pipeline and GridSearchCV.mp4 13.2 MB
  • 6. Regularization/3. Overfitting with linear models.mp4 13.1 MB
  • 4. Logistic Regression/2. Predicting a continuous response.mp4 12.1 MB
  • 4. Logistic Regression/5. Using logistic regression.mp4 11.9 MB
  • 6. Regularization/7. Regularizing logistic models.mp4 11.7 MB
  • 4. Logistic Regression/7. What is logistic regression.mp4 11.4 MB
  • 3. Linear Regression/10. Model evaluation.mp4 11.2 MB
  • 6. Regularization/5. Ridge and Lasso Regularization.mp4 9.3 MB
  • 1. Working with Machine Learning/3. Install Anaconda.mp4 9.2 MB
  • 2. Understanding Data Wrangling/2.2 311-service-requests.zip.zip 8.7 MB
  • 5. Cross Validation/3. K-fold cross-validation.mp4 8.4 MB
  • 3. Linear Regression/6. Hypothesis testing and p-values.mp4 8.2 MB
  • 5. Cross Validation/2. Traintest split.mp4 7.8 MB
  • 1. Working with Machine Learning/1. Exploring Machine Learning and its Types.mp4 7.7 MB
  • 4. Logistic Regression/9. Using logistic regression with categorical features.mp4 7.6 MB
  • 3. Linear Regression/9. Model and feature selection.mp4 7.4 MB
  • 3. Linear Regression/3. The advertising dataset.mp4 7.4 MB
  • 3. Linear Regression/7. R squared.mp4 6.0 MB
  • 3. Linear Regression/12. Summary.mp4 5.7 MB
  • 4. Logistic Regression/3. Quick refresher on linear regression.mp4 5.1 MB
  • 5. Cross Validation/5. Summary.mp4 5.1 MB
  • 3. Linear Regression/4. EDA questions on advertising data.mp4 4.9 MB
  • 6. Regularization/2. Overfitting.mp4 4.9 MB
  • 6. Regularization/9. Comparing regularized with unregularized models.mp4 3.4 MB
  • 3. Linear Regression/2. What is linear regression.mp4 3.0 MB
  • 3. Linear Regression/1. Introduction.mp4 1.8 MB
  • 4. Logistic Regression/2.1 logistic regression.zip.zip 1.4 MB
  • 6. Regularization/1. Introduction.mp4 1.2 MB
  • 4. Logistic Regression/10. Summary.mp4 918.4 kB
  • 5. Cross Validation/1. Introduction.mp4 913.1 kB
  • 4. Logistic Regression/1. Introduction.mp4 912.7 kB
  • 2. Understanding Data Wrangling/10. Summary.mp4 552.4 kB
  • 2. Understanding Data Wrangling/1. Introduction.mp4 510.5 kB
  • 2. Understanding Data Wrangling/9.2 weather_2012.csv.csv 503.8 kB
  • 2. Understanding Data Wrangling/2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip 405.1 kB
  • 6. Regularization/2.1 regularization.zip.zip 375.5 kB
  • 2. Understanding Data Wrangling/8.2 popularity-contest.tsv.tsv 189.7 kB
  • 3. Linear Regression/3.1 linear regression.zip.zip 180.5 kB
  • 1. Working with Machine Learning/5.1 A quick tour of IPython Notebook.zip.zip 105.3 kB
  • 2. Understanding Data Wrangling/6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip 80.2 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 79.6 kB
  • 2. Understanding Data Wrangling/3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip 39.7 kB
  • 5. Cross Validation/2.1 cross validation.zip.zip 24.4 kB
  • 2. Understanding Data Wrangling/4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip 18.5 kB
  • 2. Understanding Data Wrangling/5.2 bikes.csv.csv 13.8 kB
  • 2. Understanding Data Wrangling/7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip 11.4 kB
  • 3. Linear Regression/5. Simple Linear Regression.vtt 10.1 kB
  • 2. Understanding Data Wrangling/7. Cleaning Messy Data.vtt 9.7 kB
  • 1. Working with Machine Learning/5. Python and Jupyter Demo.vtt 9.5 kB
  • 3. Linear Regression/11. Handling categorical features.vtt 8.7 kB
  • 2. Understanding Data Wrangling/9. Loading data from SQL databases.vtt 7.6 kB
  • 5. Cross Validation/4. Cross-validation continued.vtt 7.2 kB
  • 6. Regularization/4. Regularizing linear models.vtt 7.1 kB
  • 2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.vtt 6.8 kB
  • 2. Understanding Data Wrangling/6. Which month was the snowiest.vtt 6.7 kB
  • 4. Logistic Regression/8. Interpreting logistic regression.vtt 6.4 kB
  • 2. Understanding Data Wrangling/4. Which borough has the most noise complaints.vtt 6.4 kB
  • 6. Regularization/3. Overfitting with linear models.vtt 6.2 kB
  • 2. Understanding Data Wrangling/2. Reading from a CSV.vtt 6.0 kB
  • 1. Working with Machine Learning/1. Exploring Machine Learning and its Types.vtt 6.0 kB
  • 4. Logistic Regression/4. Predicting a categorical response.vtt 5.9 kB
  • 2. Understanding Data Wrangling/5. Which weekday do people bike the most.vtt 5.9 kB
  • 6. Regularization/6. Regularization using scikit-learn.vtt 5.8 kB
  • 1. Working with Machine Learning/3. Install Anaconda.vtt 5.7 kB
  • 4. Logistic Regression/6. Probability, odds, log-odds.vtt 5.7 kB
  • 3. Linear Regression/8. Multiple linear regression.vtt 5.4 kB
  • 3. Linear Regression/10. Model evaluation.vtt 4.9 kB
  • 4. Logistic Regression/7. What is logistic regression.vtt 4.9 kB
  • 2. Understanding Data Wrangling/8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip 4.5 kB
  • 2. Understanding Data Wrangling/8. How to deal with timestamps.vtt 4.5 kB
  • 2. Understanding Data Wrangling/9.1 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip 4.3 kB
  • 4. Logistic Regression/2. Predicting a continuous response.vtt 4.2 kB
  • 6. Regularization/8. Pipeline and GridSearchCV.vtt 4.0 kB
  • 4. Logistic Regression/5. Using logistic regression.vtt 4.0 kB
  • 5. Cross Validation/3. K-fold cross-validation.vtt 3.8 kB
  • 5. Cross Validation/2. Traintest split.vtt 3.7 kB
  • 6. Regularization/5. Ridge and Lasso Regularization.vtt 3.5 kB
  • 3. Linear Regression/9. Model and feature selection.vtt 3.4 kB
  • 3. Linear Regression/3. The advertising dataset.vtt 3.1 kB
  • 3. Linear Regression/6. Hypothesis testing and p-values.vtt 3.0 kB
  • 3. Linear Regression/12. Summary.vtt 2.9 kB
  • 4. Logistic Regression/9. Using logistic regression with categorical features.vtt 2.7 kB
  • 3. Linear Regression/7. R squared.vtt 2.7 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 1.9 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

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