[Tutorialsplanet NET] Udemy Beginning with Machine Learning & Data Science in Python Torrent Download Locations

[Tutorialsplanet.NET] Udemy - Beginning with Machine Learning & Data Science in Python
    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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