[DesireCourse.Net] Udemy - Beginning with Machine Learning & Data Science in Python
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2021-3-25 12:31 | 2025-1-2 05:26 | 156 | 531.1 MB | 50 |
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文件列表
- 1. Working with Machine Learning/1. Exploring Machine Learning and its Types.mp47.32MB
- 1. Working with Machine Learning/3. Install Anaconda.mp48.78MB
- 1. Working with Machine Learning/5. Python and Jupyter Demo.mp417.69MB
- 2. Understanding Data Wrangling/1. Introduction.mp4498.56KB
- 2. Understanding Data Wrangling/10. Summary.mp4539.41KB
- 2. Understanding Data Wrangling/2. Reading from a CSV.mp416.06MB
- 2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.mp425.11MB
- 2. Understanding Data Wrangling/4. Which borough has the most noise complaints.mp419.48MB
- 2. Understanding Data Wrangling/5. Which weekday do people bike the most.mp416.96MB
- 2. Understanding Data Wrangling/6. Which month was the snowiest.mp420.43MB
- 2. Understanding Data Wrangling/7. Cleaning Messy Data.mp432.02MB
- 2. Understanding Data Wrangling/8. How to deal with timestamps.mp416.37MB
- 2. Understanding Data Wrangling/8.2 popularity-contest.tsv.tsv185.24KB
- 2. Understanding Data Wrangling/9. Loading data from SQL databases.mp413.45MB
- 3. Linear Regression/1. Introduction.mp41.74MB
- 3. Linear Regression/10. Model evaluation.mp410.73MB
- 3. Linear Regression/11. Handling categorical features.mp419.83MB
- 3. Linear Regression/12. Summary.mp45.45MB
- 3. Linear Regression/2. What is linear regression.mp42.83MB
- 3. Linear Regression/3. The advertising dataset.mp47.09MB
- 3. Linear Regression/4. EDA questions on advertising data.mp44.72MB
- 3. Linear Regression/5. Simple Linear Regression.mp421.9MB
- 3. Linear Regression/6. Hypothesis testing and p-values.mp47.83MB
- 3. Linear Regression/7. R squared.mp45.77MB
- 3. Linear Regression/8. Multiple linear regression.mp415.34MB
- 3. Linear Regression/9. Model and feature selection.mp47.09MB
- 4. Logistic Regression/1. Introduction.mp4891.31KB
- 4. Logistic Regression/10. Summary.mp4896.84KB
- 4. Logistic Regression/2. Predicting a continuous response.mp411.59MB
- 4. Logistic Regression/3. Quick refresher on linear regression.mp44.9MB
- 4. Logistic Regression/4. Predicting a categorical response.mp415.7MB
- 4. Logistic Regression/5. Using logistic regression.mp411.37MB
- 4. Logistic Regression/6. Probability, odds, log-odds.mp415.05MB
- 4. Logistic Regression/7. What is logistic regression.mp410.91MB
- 4. Logistic Regression/8. Interpreting logistic regression.mp416.3MB
- 4. Logistic Regression/9. Using logistic regression with categorical features.mp47.25MB
- 5. Cross Validation/1. Introduction.mp4891.74KB
- 5. Cross Validation/2. Traintest split.mp47.45MB
- 5. Cross Validation/3. K-fold cross-validation.mp48.03MB
- 5. Cross Validation/4. Cross-validation continued.mp415.89MB
- 5. Cross Validation/5. Summary.mp44.87MB
- 6. Regularization/1. Introduction.mp41.17MB
- 6. Regularization/2. Overfitting.mp44.7MB
- 6. Regularization/3. Overfitting with linear models.mp412.52MB
- 6. Regularization/4. Regularizing linear models.mp416.89MB
- 6. Regularization/5. Ridge and Lasso Regularization.mp48.86MB
- 6. Regularization/6. Regularization using scikit-learn.mp422.89MB
- 6. Regularization/7. Regularizing logistic models.mp411.18MB
- 6. Regularization/8. Pipeline and GridSearchCV.mp412.59MB
- 6. Regularization/9. Comparing regularized with unregularized models.mp43.22MB
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