[DesireCourse Net] Udemy Complete Data Science Training with Python for Data Analysis Torrent Download Locations

[DesireCourse.Net] Udemy - Complete Data Science Training with Python for Data Analysis
    1. Introduction to the Data Science in Python Bootcamp
          1. What is Data Science.mp4 -

          1. What is Data Science.vtt -

          2. Introduction to the Course Instructor.m4v -

          2. Introduction to the Course Instructor.vtt -

          3. Data For the Course.html -

          3.1 scriptsLecture.zip.zip -

          4. Introduction to the Python Data Science Tool.mp4 -

          4. Introduction to the Python Data Science Tool.vtt -

          5. For Mac Users.mp4 -

          5. For Mac Users.vtt -

          6. Introduction to the Python Data Science Environment.mp4 -

          6. Introduction to the Python Data Science Environment.vtt -

          7. Some Miscellaneous IPython Usage Facts.mp4 -

          7. Some Miscellaneous IPython Usage Facts.vtt -

          8. Online iPython Interpreter.mp4 -

          8. Online iPython Interpreter.vtt -

          9. Conclusion to Section 1.mp4 -

          9. Conclusion to Section 1.vtt -

    10. Unsupervised Learning in Python
          1. Unsupervised Classification- Some Basic Ideas.mp4 -

          1. Unsupervised Classification- Some Basic Ideas.vtt -

          10. Principal Component Analysis (PCA)-Practical Implementation.mp4 -

          10. Principal Component Analysis (PCA)-Practical Implementation.vtt -

          11. Conclusions to Section 10.mp4 -

          11. Conclusions to Section 10.vtt -

          2. KMeans-theory.mp4 -

          2. KMeans-theory.vtt -

          3. KMeans-implementation on the iris data.mp4 -

          3. KMeans-implementation on the iris data.vtt -

          4. Quantifying KMeans Clustering Performance.mp4 -

          4. Quantifying KMeans Clustering Performance.vtt -

          5. KMeans Clustering with Real Data.mp4 -

          5. KMeans Clustering with Real Data.vtt -

          6. How Do We Select the Number of Clusters.mp4 -

          6. How Do We Select the Number of Clusters.vtt -

          7. Hierarchical Clustering-theory.mp4 -

          7. Hierarchical Clustering-theory.vtt -

          8. Hierarchical Clustering-practical.mp4 -

          8. Hierarchical Clustering-practical.vtt -

          9. Principal Component Analysis (PCA)-Theory.mp4 -

          9. Principal Component Analysis (PCA)-Theory.vtt -

    11. Supervised Learning
          1. What is This Section About.mp4 -

          1. What is This Section About.vtt -

          10. knn-Classification.mp4 -

          10. knn-Classification.vtt -

          11. knn-Regression.mp4 -

          11. knn-Regression.vtt -

          12. Gradient Boosting-classification.mp4 -

          12. Gradient Boosting-classification.vtt -

          13. Gradient Boosting-regression.mp4 -

          13. Gradient Boosting-regression.vtt -

          14. Voting Classifier.mp4 -

          14. Voting Classifier.vtt -

          15. Conclusions to Section 11.mp4 -

          15. Conclusions to Section 11.vtt -

          16. Section 11 Quiz.html -

          2. Data Preparation for Supervised Learning.mp4 -

          2. Data Preparation for Supervised Learning.vtt -

          3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4 -

          3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt -

          4. Using Logistic Regression as a Classification Model.mp4 -

          4. Using Logistic Regression as a Classification Model.vtt -

          5. RF-Classification.mp4 -

          5. RF-Classification.vtt -

          6. RF-Regression.mp4 -

          6. RF-Regression.vtt -

          7. SVM- Linear Classification.mp4 -

          7. SVM- Linear Classification.vtt -

          8. SVM- Non Linear Classification.mp4 -

          8. SVM- Non Linear Classification.vtt -

          9. Support Vector Regression.mp4 -

          9. Support Vector Regression.vtt -

    12. Artificial Neural Networks (ANN) and Deep Learning (DL)
          1. Theory Behind ANN and DNN.mp4 -

          1. Theory Behind ANN and DNN.vtt -

          10. Specify the Activation Function.mp4 -

          10. Specify the Activation Function.vtt -

          11. H2O Deep Learning For Predictions.mp4 -

          11. H2O Deep Learning For Predictions.vtt -

          12. Conclusions to Section 12.mp4 -

          12. Conclusions to Section 12.vtt -

          13. Section 12 Quiz.html -

          2. Perceptrons for Binary Classification.mp4 -

          2. Perceptrons for Binary Classification.vtt -

          3. Getting Started with ANN-binary classification.mp4 -

          3. Getting Started with ANN-binary classification.vtt -

          4. Multi-label classification with MLP.mp4 -

          4. Multi-label classification with MLP.vtt -

          5. Regression with MLP.mp4 -

          5. Regression with MLP.vtt -

          6. MLP with PCA on a Large Dataset.mp4 -

          6. MLP with PCA on a Large Dataset.vtt -

          7. Start With Deep Neural Network (DNN).html -

          8. Start with H20.mp4 -

          8. Start with H20.vtt -

          9. Default H2O Deep Learning Algorithm.mp4 -

          9. Default H2O Deep Learning Algorithm.vtt -

    13. Miscellaneous Lectures Information
          1. Data For This Section.html -

          2. Read in Data from Online CSV.mp4 -

          2. Read in Data from Online CSV.vtt -

          3. Read Data from a Database.mp4 -

          3. Read Data from a Database.vtt -

          4. Naive Bayes Classification.m4v -

          4. Naive Bayes Classification.vtt -

          5. Data Imputation.m4v -

          5. Data Imputation.vtt -

    2. Introduction to Python Pre-Requisites for Data Science
          1. Rationale Behind This Section.html -

          2. Different Types of Data Used in Statistical ML Analysis.mp4 -

          2. Different Types of Data Used in Statistical ML Analysis.vtt -

          3. Different Types of Data Used Programatically.mp4 -

          3. Different Types of Data Used Programatically.vtt -

          4. Python Data Science Packages To Be Used.mp4 -

          4. Python Data Science Packages To Be Used.vtt -

          5. Conclusions to Section 2.mp4 -

          5. Conclusions to Section 2.vtt -

    3. Introduction to Numpy
          1. Numpy Introduction.mp4 -

          1. Numpy Introduction.vtt -

          10. Conclusion to Section 3.mp4 -

          10. Conclusion to Section 3.vtt -

          11. Section 3 Quiz.html -

          2. Create Numpy Arrays.mp4 -

          2. Create Numpy Arrays.vtt -

          3. Numpy Operations.mp4 -

          3. Numpy Operations.vtt -

          4. Matrix Arithmetic and Linear Systems.mp4 -

          4. Matrix Arithmetic and Linear Systems.vtt -

          5. Numpy for Basic Vector Arithmetric.mp4 -

          5. Numpy for Basic Vector Arithmetric.vtt -

          6. Numpy for Basic Matrix Arithmetic.mp4 -

          6. Numpy for Basic Matrix Arithmetic.vtt -

          7. Broadcasting with Numpy.mp4 -

          7. Broadcasting with Numpy.vtt -

          8. Solve Equations with Numpy.mp4 -

          8. Solve Equations with Numpy.vtt -

          9. Numpy for Statistical Operation.mp4 -

          9. Numpy for Statistical Operation.vtt -

    4. Introduction to Pandas
          1. Data Structures in Python.mp4 -

          1. Data Structures in Python.vtt -

          2. Read in Data.html -

          3. Read in CSV Data Using Pandas.mp4 -

          3. Read in CSV Data Using Pandas.vtt -

          4. Read in Excel Data Using Pandas.mp4 -

          4. Read in Excel Data Using Pandas.vtt -

          5. Reading in JSON Data.mp4 -

          5. Reading in JSON Data.vtt -

          6. Read in HTML Data.mp4 -

          6. Read in HTML Data.vtt -

          7. Conclusion to Section 4.mp4 -

          7. Conclusion to Section 4.vtt -

    5. Data Pre-ProcessingWrangling
          1. Rationale behind this section.mp4 -

          1. Rationale behind this section.vtt -

          10. Rank and Sort Data.mp4 -