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[Tutorialsplanet NET] Udemy - Beginning with Machine Learning & Data Science in Python
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Torrent Files List
1. Working with Machine Learning (Size: 542.82 MB) (Files: 120)
1. Working with Machine Learning
1. Exploring Machine Learning and its Types.mp4
1. Exploring Machine Learning and its Types.vtt
2. Machine Learning Foundations.html
3. Install Anaconda.mp4
3. Install Anaconda.vtt
4. Python Versions.html
5. Python and Jupyter Demo.mp4
5. Python and Jupyter Demo.vtt
5.1 A quick tour of IPython Notebook.zip.zip
6. Python Basics.html
2. Understanding Data Wrangling
1. Introduction.mp4
1. Introduction.vtt
10. Summary.mp4
10. Summary.vtt
2. Reading from a CSV.mp4
2. Reading from a CSV.vtt
2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip
2.2 311-service-requests.zip.zip
3. Selecting data and finding the most common complaint type.mp4
3. Selecting data and finding the most common complaint type.vtt
3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip
4. Which borough has the most noise complaints.mp4
4. Which borough has the most noise complaints.vtt
4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip
5. Which weekday do people bike the most.mp4
5. Which weekday do people bike the most.vtt
5.1 bikes.csv.csv
5.2 Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb.zip.zip
6. Which month was the snowiest.mp4
6. Which month was the snowiest.vtt
6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip
7. Cleaning Messy Data.mp4
7. Cleaning Messy Data.vtt
7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip
8. How to deal with timestamps.mp4
8. How to deal with timestamps.vtt
8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip
8.2 popularity-contest.tsv.tsv
9. Loading data from SQL databases.mp4
9. Loading data from SQL databases.vtt
9.1 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip
9.2 weather_2012_sqlite.zip.zip
9.3 weather_2012.csv.csv
3. Linear Regression
1. Introduction.mp4
1. Introduction.vtt
10. Model evaluation.mp4
10. Model evaluation.vtt
11. Handling categorical features.mp4
11. Handling categorical features.vtt
12. Summary.mp4
12. Summary.vtt
2. What is linear regression.mp4
2. What is linear regression.vtt
3. The advertising dataset.mp4
3. The advertising dataset.vtt
3.1 linear regression.zip.zip
4. EDA questions on advertising data.mp4
4. EDA questions on advertising data.vtt
5. Simple Linear Regression.mp4
5. Simple Linear Regression.vtt
6. Hypothesis testing and p-values.mp4
6. Hypothesis testing and p-values.vtt
7. R squared.mp4
7. R squared.vtt
8. Multiple linear regression.mp4
8. Multiple linear regression.vtt
9. Model and feature selection.mp4
9. Model and feature selection.vtt
4. Logistic Regression
1. Introduction.mp4
1. Introduction.vtt
10. Summary.mp4
10. Summary.vtt
2. Predicting a continuous response.mp4
2. Predicting a continuous response.vtt
2.1 logistic regression.zip.zip
3. Quick refresher on linear regression.mp4
3. Quick refresher on linear regression.vtt
4. Predicting a categorical response.mp4
4. Predicting a categorical response.vtt
5. Using logistic regression.mp4
5. Using logistic regression.vtt
6. Probability, odds, log-odds.mp4
6. Probability, odds, log-odds.vtt
7. What is logistic regression.mp4
7. What is logistic regression.vtt
8. Interpreting logistic regression.mp4
8. Interpreting logistic regression.vtt
9. Using logistic regression with categorical features.mp4
9. Using logistic regression with categorical features.vtt
5. Cross Validation
1. Introduction.mp4
1. Introduction.vtt
2. Traintest split.mp4
2. Traintest split.vtt
2.1 cross validation.zip.zip
3. K-fold cross-validation.mp4
3. K-fold cross-validation.vtt
4. Cross-validation continued.mp4
4. Cross-validation continued.vtt
5. Summary.mp4
5. Summary.vtt
6. Regularization
1. Introduction.mp4
1. Introduction.vtt
2. Overfitting.mp4
2. Overfitting.vtt
2.1 regularization.zip.zip
3. Overfitting with linear models.mp4
3. Overfitting with linear models.vtt
4. Regularizing linear models.mp4
4. Regularizing linear models.vtt
5. Ridge and Lasso Regularization.mp4
5. Ridge and Lasso Regularization.vtt
6. Regularization using scikit-learn.mp4
6. Regularization using scikit-learn.vtt
7. Regularizing logistic models.mp4
7. Regularizing logistic models.vtt
8. Pipeline and GridSearchCV.mp4
8. Pipeline and GridSearchCV.vtt
9. Comparing regularized with unregularized models.mp4
9. Comparing regularized with unregularized models.vtt
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