Udemy Beginning with Machine Learning Data Science in Python | E3B431B036D4736093322E22D86F59CA5AEF761A

Udemy Beginning with Machine Learning Data Science in Python

Name	Udemy - Beginning with Machine Learning & Data Science in Python	DOWNLOAD Copy Link
Total Size	569.0 MB
Total Files	119
Hash	E3B431B036D4736093322E22D86F59CA5AEF761A

/1. Working with Machine Learning/
1. Exploring Machine Learning and its Types.mp4	7.7 MB
1. Exploring Machine Learning and its Types.vtt	6.0 KB
2. Machine Learning Foundations.html	0.2 KB
3. Install Anaconda.mp4	9.2 MB
3. Install Anaconda.vtt	5.7 KB
4. Python Versions.html	0.2 KB
5. Python and Jupyter Demo.mp4	18.5 MB
5. Python and Jupyter Demo.vtt	9.5 KB
5.1 A quick tour of IPython Notebook.zip.zip	105.3 KB
6. Python Basics.html	0.2 KB
/2. Understanding Data Wrangling/
1. Introduction.mp4	510.5 KB
1. Introduction.vtt	0.3 KB
10. Summary.mp4	552.4 KB
10. Summary.vtt	0.4 KB
2. Reading from a CSV.mp4	16.8 MB
2. Reading from a CSV.vtt	6.0 KB
2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip	405.2 KB
2.2 311-service-requests.zip.zip	8.7 MB
3. Selecting data and finding the most common complaint type.mp4	26.3 MB
3. Selecting data and finding the most common complaint type.vtt	6.8 KB
3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip	39.7 KB
4. Which borough has the most noise complaints.mp4	20.4 MB
4. Which borough has the most noise complaints.vtt	6.4 KB
4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip	18.5 KB
5. Which weekday do people bike the most.mp4	17.8 MB
5. Which weekday do people bike the most.vtt	5.9 KB
5.1 bikes.csv.csv	13.8 KB
5.2 Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb.zip.zip	79.6 KB
6. Which month was the snowiest.mp4	21.4 MB
6. Which month was the snowiest.vtt	6.7 KB
6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip	80.2 KB
7. Cleaning Messy Data.mp4	33.6 MB
7. Cleaning Messy Data.vtt	9.7 KB
7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip	11.4 KB
8. How to deal with timestamps.mp4	17.2 MB
8. How to deal with timestamps.vtt	4.5 KB
8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip	4.5 KB
8.2 popularity-contest.tsv.tsv	189.7 KB
9. Loading data from SQL databases.mp4	14.1 MB
9. Loading data from SQL databases.vtt	7.6 KB
9.1 weather_2012_sqlite.zip.zip	1.4 KB
9.2 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip	4.3 KB
9.3 weather_2012.csv.csv	503.8 KB
/3. Linear Regression/
1. Introduction.mp4	1.8 MB
1. Introduction.vtt	1.2 KB
10. Model evaluation.mp4	11.2 MB
10. Model evaluation.vtt	4.9 KB
11. Handling categorical features.mp4	20.8 MB
11. Handling categorical features.vtt	8.7 KB
12. Summary.mp4	5.7 MB
12. Summary.vtt	2.9 KB
2. What is linear regression.mp4	3.0 MB
2. What is linear regression.vtt	1.7 KB
3. The advertising dataset.mp4	7.4 MB
3. The advertising dataset.vtt	3.1 KB
3.1 linear regression.zip.zip	180.5 KB
4. EDA questions on advertising data.mp4	4.9 MB
4. EDA questions on advertising data.vtt	1.8 KB
5. Simple Linear Regression.mp4	23.0 MB
5. Simple Linear Regression.vtt	10.1 KB
6. Hypothesis testing and p-values.mp4	8.2 MB
6. Hypothesis testing and p-values.vtt	3.0 KB
7. R squared.mp4	6.0 MB
7. R squared.vtt	2.7 KB
8. Multiple linear regression.mp4	16.1 MB
8. Multiple linear regression.vtt	5.4 KB
9. Model and feature selection.mp4	7.4 MB
9. Model and feature selection.vtt	3.4 KB
/4. Logistic Regression/
1. Introduction.mp4	912.7 KB
1. Introduction.vtt	0.5 KB
10. Summary.mp4	918.4 KB
10. Summary.vtt	0.4 KB
2. Predicting a continuous response.mp4	12.1 MB
2. Predicting a continuous response.vtt	4.2 KB
2.1 logistic regression.zip.zip	1.4 MB
3. Quick refresher on linear regression.mp4	5.1 MB
3. Quick refresher on linear regression.vtt	1.3 KB
4. Predicting a categorical response.mp4	16.5 MB
4. Predicting a categorical response.vtt	5.9 KB
5. Using logistic regression.mp4	11.9 MB
5. Using logistic regression.vtt	4.0 KB
6. Probability, odds, log-odds.mp4	15.8 MB
6. Probability, odds, log-odds.vtt	5.7 KB
7. What is logistic regression.mp4	11.4 MB
7. What is logistic regression.vtt	4.9 KB
8. Interpreting logistic regression.mp4	17.1 MB
8. Interpreting logistic regression.vtt	6.4 KB
9. Using logistic regression with categorical features.mp4	7.6 MB
9. Using logistic regression with categorical features.vtt	2.7 KB
/5. Cross Validation/
1. Introduction.mp4	913.1 KB
1. Introduction.vtt	0.5 KB
2. Traintest split.mp4	7.8 MB
2. Traintest split.vtt	3.7 KB
2.1 cross validation.zip.zip	24.4 KB
3. K-fold cross-validation.mp4	8.4 MB
3. K-fold cross-validation.vtt	3.8 KB
4. Cross-validation continued.mp4	16.7 MB
4. Cross-validation continued.vtt	7.2 KB
5. Summary.mp4	5.1 MB
5. Summary.vtt	2.1 KB
/6. Regularization/
1. Introduction.mp4	1.2 MB
1. Introduction.vtt	0.7 KB
2. Overfitting.mp4	4.9 MB
2. Overfitting.vtt	2.4 KB
2.1 regularization.zip.zip	375.5 KB
3. Overfitting with linear models.mp4	13.1 MB
3. Overfitting with linear models.vtt	6.2 KB
4. Regularizing linear models.mp4	17.7 MB
4. Regularizing linear models.vtt	7.1 KB
5. Ridge and Lasso Regularization.mp4	9.3 MB
5. Ridge and Lasso Regularization.vtt	3.5 KB
6. Regularization using scikit-learn.mp4	24.0 MB
6. Regularization using scikit-learn.vtt	5.8 KB
7. Regularizing logistic models.mp4	11.7 MB
7. Regularizing logistic models.vtt	2.1 KB
8. Pipeline and GridSearchCV.mp4	13.2 MB
8. Pipeline and GridSearchCV.vtt	4.0 KB
9. Comparing regularized with unregularized models.mp4	3.4 MB
9. Comparing regularized with unregularized models.vtt	1.9 KB
Total files 119