| 1. Data For This Section.html | 102.4 B | ||
| 1. Data Structures in Python.mp4 | 25.1 MB | ||
| 1. Data Structures in Python.vtt | 10 KB | ||
| 1. How is Machine Learning Different from Statistical Data Analysis.mp4 | 13.7 MB | ||
| 1. How is Machine Learning Different from Statistical Data Analysis.vtt | 6.2 KB | ||
| 1. Numpy Introduction.mp4 | 8.7 MB | ||
| 1. Numpy Introduction.vtt | 3.8 KB | ||
| 1. Rationale Behind This Section.html | 409.6 B | ||
| 1. Rationale behind this section.mp4 | 8.1 MB | ||
| 1. Rationale behind this section.vtt | 4.6 KB | ||
| 1. Theory Behind ANN and DNN.mp4 | 22.6 MB | ||
| 1. Theory Behind ANN and DNN.vtt | 9.9 KB | ||
| 1. Unsupervised Classification- Some Basic Ideas.mp4 | 6.2 MB | ||
| 1. Unsupervised Classification- Some Basic Ideas.vtt | 1.8 KB | ||
| 1. What is Data Science.mp4 | 8.4 MB | ||
| 1. What is Data Science.vtt | 4 KB | ||
| 1. What is Data Visualization.mp4 | 20.7 MB | ||
| 1. What is Data Visualization.vtt | 9.8 KB | ||
| 1. What is Hypothesis Testing.mp4 | 13.4 MB | ||
| 1. What is Hypothesis Testing.vtt | 5.8 KB | ||
| 1. What is Statistical Data Analysis.mp4 | 25.3 MB | ||
| 1. What is Statistical Data Analysis.vtt | 9.6 KB | ||
| 1. What is This Section About.mp4 | 24.9 MB | ||
| 1. What is This Section About.vtt | 11.5 KB | ||
| 10. Conclusion to Section 3.mp4 | 6.2 MB | ||
| 10. Conclusion to Section 3.vtt | 2.5 KB | ||
| 10. Polynomial Regression.mp4 | 9.2 MB | ||
| 10. Polynomial Regression.vtt | 3.7 KB | ||
| 10. Principal Component Analysis (PCA)-Practical Implementation.mp4 | 9.1 MB | ||
| 10. Principal Component Analysis (PCA)-Practical Implementation.vtt | 4.2 KB | ||
| 10. Rank and Sort Data.mp4 | 24.3 MB | ||
| 10. Rank and Sort Data.vtt | 7.3 KB | ||
| 10. Specify the Activation Function.mp4 | 6.2 MB | ||
| 10. Specify the Activation Function.vtt | 2.2 KB | ||
| 10. Standard Normal Distribution and Z-scores.mp4 | 9.8 MB | ||
| 10. Standard Normal Distribution and Z-scores.vtt | 4.2 KB | ||
| 10. knn-Classification.mp4 | 18.2 MB | ||
| 10. knn-Classification.vtt | 8 KB | ||
| 11. Concatenate.mp4 | 23.7 MB | ||
| 11. Concatenate.vtt | 8 KB | ||
| 11. Conclusions to Section 10.mp4 | 5.5 MB | ||
| 11. Conclusions to Section 10.vtt | 2.5 KB | ||
| 11. Confidence Interval-Theory.mp4 | 13.7 MB | ||
| 11. Confidence Interval-Theory.vtt | 5.9 KB | ||
| 11. GLM Generalized Linear Model.mp4 | 11.8 MB | ||
| 11. GLM Generalized Linear Model.vtt | 5.2 KB | ||
| 11. H2O Deep Learning For Predictions.mp4 | 12 MB | ||
| 11. H2O Deep Learning For Predictions.vtt | 5.2 KB | ||
| 11. Section 3 Quiz.html | 204.8 B | ||
| 11. knn-Regression.mp4 | 8.4 MB | ||
| 11. knn-Regression.vtt | 4 KB | ||
| 12. Conclusions to Section 12.mp4 | 5.2 MB | ||
| 12. Conclusions to Section 12.vtt | 2.1 KB | ||
| 12. Confidence Interval-Calculation.mp4 | 13.6 MB | ||
| 12. Confidence Interval-Calculation.vtt | 5.8 KB | ||
| 12. Gradient Boosting-classification.mp4 | 15 MB | ||
| 12. Gradient Boosting-classification.vtt | 6 KB | ||
| 12. Logistic Regression.mp4 | 28.8 MB | ||
| 12. Logistic Regression.vtt | 11.1 KB | ||
| 12. Merging and Joining Data Frames.mp4 | 28.8 MB | ||
| 12. Merging and Joining Data Frames.vtt | 10.7 KB | ||
| 13. Conclusion to Section 5.mp4 | 5.4 MB | ||
| 13. Conclusion to Section 5.vtt | 2.2 KB | ||
| 13. Conclusions to Section 7.mp4 | 3.8 MB | ||
| 13. Conclusions to Section 7.vtt | 1.6 KB | ||
| 13. Conclusions to Section 8.mp4 | 4.9 MB | ||
| 13. Conclusions to Section 8.vtt | 2 KB | ||
| 13. Gradient Boosting-regression.mp4 | 10.9 MB | ||
| 13. Gradient Boosting-regression.vtt | 3.7 KB | ||
| 13. Section 12 Quiz.html | 204.8 B | ||
| 14. Section 8 Quiz.html | 204.8 B | ||
| 14. Voting Classifier.mp4 | 9.5 MB | ||
| 14. Voting Classifier.vtt | 3.8 KB | ||
| 15. Conclusions to Section 11.mp4 | 7.2 MB | ||
| 15. Conclusions to Section 11.vtt | 2.9 KB | ||
| 16. Section 11 Quiz.html | 204.8 B | ||
| 2. Create Numpy Arrays.mp4 | 20.9 MB | ||
| 2. Create Numpy Arrays.vtt | 5.9 KB | ||
| 2. Data Preparation for Supervised Learning.mp4 | 28.3 MB | ||
| 2. Data Preparation for Supervised Learning.vtt | 10.1 KB | ||
| 2. Different Types of Data Used in Statistical & ML Analysis.mp4 | 9.4 MB | ||
| 2. Different Types of Data Used in Statistical & ML Analysis.vtt | 3.7 KB | ||
| 2. Introduction to the Course & Instructor.mp4 | 29.6 MB | ||
| 2. Introduction to the Course & Instructor.vtt | 13.5 KB | ||
| 2. KMeans-theory.mp4 | 5.1 MB | ||
| 2. KMeans-theory.vtt | 2.5 KB | ||
| 2. Perceptrons for Binary Classification.mp4 | 10 MB | ||
| 2. Perceptrons for Binary Classification.vtt | 4.7 KB | ||
| 2. Read in Data from Online CSV.mp4 | 6.7 MB | ||
| 2. Read in Data from Online CSV.vtt | 3.9 KB | ||
| 2. Read in Data.html | 204.8 B | ||
| 2. Removing NAsNo Values From Our Data.mp4 | 19.3 MB | ||
| 2. Removing NAsNo Values From Our Data.vtt | 6.4 KB | ||
| 2. Some Pointers on Collecting Data for Statistical Studies.mp4 | 20.9 MB | ||
| 2. Some Pointers on Collecting Data for Statistical Studies.vtt | 9.1 KB | ||
| 2. Some Theoretical Principles Behind Data Visualization.mp4 | 16.6 MB | ||
| 2. Some Theoretical Principles Behind Data Visualization.vtt | 7.1 KB | ||
| 2. Test the Difference Between Two Groups.mp4 | 17.8 MB | ||
| 2. Test the Difference Between Two Groups.vtt | 7.3 KB | ||
| 2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4 | 15.8 MB | ||
| 2. What is Machine Learning (ML) About Some Theoretical Pointers.vtt | 6.6 KB | ||
| 3. Basic Data Handling Starting with Conditional Data Selection.mp4 | 14.9 MB | ||
| 3. Basic Data Handling Starting with Conditional Data Selection.vtt | 4.1 KB | ||
| 3. Data For the Course.html | 102.4 B | ||
| 3. Different Types of Data Used Programatically.mp4 | 7.7 MB | ||
| 3. Different Types of Data Used Programatically.vtt | 3 KB | ||
| 3. Getting Started with ANN-binary classification.mp4 | 8.5 MB | ||
| 3. Getting Started with ANN-binary classification.vtt | 3.5 KB | ||
| 3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 | 29.4 MB | ||
| 3. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt | 11.9 KB | ||
| 3. KMeans-implementation on the iris data.mp4 | 19.5 MB | ||
| 3. KMeans-implementation on the iris data.vtt | 7.6 KB | ||
| 3. Numpy Operations.mp4 | 36.7 MB | ||
| 3. Numpy Operations.vtt | 15 KB | ||
| 3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4 | 24 MB | ||
| 3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt | 10.5 KB | ||
| 3. Read Data from a Database.mp4 | 12.3 MB | ||
| 3. Read Data from a Database.vtt | 7.8 KB | ||
| 3. Read in CSV Data Using Pandas.mp4 | 15.3 MB | ||
| 3. Read in CSV Data Using Pandas.vtt | 5.8 KB | ||
| 3. Some Pointers on Exploring Quantitative Data.html | 512 B | ||
| 3. Test the Difference Between More Than Two Groups.mp4 | 28.3 MB | ||
| 3. Test the Difference Between More Than Two Groups.vtt | 10.9 KB | ||
| 3.1 scriptsLecture.zip.zip | 308 MB | ||
| 4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 | 13.4 MB | ||
| 4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt | 5.5 KB | ||
| 4. Drop ColumnRow.mp4 | 15.7 MB | ||
| 4. Drop ColumnRow.vtt | 4.4 KB | ||
| 4. Explore the Quantitative Data Descriptive Statistics.mp4 | 17.4 MB | ||
| 4. Explore the Quantitative Data Descriptive Statistics.vtt | 7.6 KB | ||
| 4. Explore the Relationship Between Two Quantitative Variables.mp4 | 9.4 MB | ||
| 4. Explore the Relationship Between Two Quantitative Variables.vtt | 4.4 KB | ||
| 4. Introduction to the Python Data Science Tool.mp4 | 25 MB | ||
| 4. Introduction to the Python Data Science Tool.vtt | 10.1 KB | ||
| 4. Matrix Arithmetic and Linear Systems.mp4 | 15.8 MB | ||
| 4. Matrix Arithmetic and Linear Systems.vtt | 6.5 KB | ||
| 4. Multi-label classification with MLP.mp4 | 13.5 MB | ||
| 4. Multi-label classification with MLP.vtt | 4.8 KB | ||
| 4. Naive Bayes Classification.mp4 | 9.9 MB | ||
| 4. Naive Bayes Classification.vtt | 6.8 KB | ||
| 4. Python Data Science Packages To Be Used.mp4 | 7.9 MB | ||
| 4. Python Data Science Packages To Be Used.vtt | 3.8 KB | ||
| 4. Quantifying KMeans Clustering Performance.mp4 | 9.6 MB | ||
| 4. Quantifying KMeans Clustering Performance.vtt | 4.4 KB | ||
| 4. Read in Excel Data Using Pandas.mp4 | 11.4 MB | ||
| 4. Read in Excel Data Using Pandas.vtt | 3.8 KB | ||
| 4. Using Logistic Regression as a Classification Model.mp4 | 20.6 MB | ||
| 4. Using Logistic Regression as a Classification Model.vtt | 8.7 KB | ||
| 5. Conclusions to Section 2.mp4 | 4.9 MB | ||
| 5. Conclusions to Section 2.vtt | 2.4 KB | ||
| 5. Correlation Analysis.mp4 | 20.7 MB | ||
| 5. Correlation Analysis.vtt | 8.6 KB | ||
| 5. Data Imputation.mp4 | 56.4 MB | ||
| 5. Data Imputation.vtt | 9 KB | ||
| 5. For Mac Users.mp4 | 10.2 MB | ||
| 5. For Mac Users.vtt | 3.9 KB | ||
| 5. Grouping & Summarizing Data by Categories.mp4 | 33.1 MB | ||
| 5. Grouping & Summarizing Data by Categories.vtt | 10.3 KB | ||
| 5. KMeans Clustering with Real Data.mp4 | 12.1 MB | ||
| 5. KMeans Clustering with Real Data.vtt | 4.5 KB | ||
| 5. Numpy for Basic Vector Arithmetric.mp4 | 11.8 MB | ||
| 5. Numpy for Basic Vector Arithmetric.vtt | 3.8 KB | ||
| 5. RF-Classification.mp4 | 28.5 MB | ||
| 5. RF-Classification.vtt | 12.2 KB | ||
| 5. Reading in JSON Data.mp4 | 18.7 MB | ||
| 5. Reading in JSON Data.vtt | 3.1 KB | ||
| 5. Regression with MLP.mp4 | 9 MB | ||
| 5. Regression with MLP.vtt | 3.5 KB | ||
| 5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4 | 29.8 MB | ||
| 5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.vtt | 12.2 KB | ||
| 5. Subset and Index Data.mp4 | 28 MB | ||
| 5. Subset and Index Data.vtt | 7.8 KB | ||
| 6. Barplot.mp4 | 53.8 MB | ||
| 6. Barplot.vtt | 22.3 KB | ||
| 6. Basic Data Grouping Based on Qualitative Attributes.mp4 | 26.6 MB | ||
| 6. Basic Data Grouping Based on Qualitative Attributes.vtt | 8.3 KB | ||
| 6. How Do We Select the Number of Clusters.mp4 | 19 MB | ||
| 6. How Do We Select the Number of Clusters.vtt | 4.2 KB | ||
| 6. Introduction to the Python Data Science Environment.mp4 | 40.3 MB | ||
| 6. Introduction to the Python Data Science Environment.vtt | 17.2 KB | ||
| 6. Linear Regression-Theory.mp4 | 24.9 MB | ||
| 6. Linear Regression-Theory.vtt | 9.8 KB | ||
| 6. MLP with PCA on a Large Dataset.mp4 | 19.2 MB | ||
| 6. MLP with PCA on a Large Dataset.vtt | 7.6 KB | ||
| 6. Numpy for Basic Matrix Arithmetic.mp4 | 13.9 MB | ||
| 6. Numpy for Basic Matrix Arithmetic.vtt | 5.2 KB | ||
| 6. RF-Regression.mp4 | 23.6 MB | ||
| 6. RF-Regression.vtt | 9.7 KB | ||
| 6. Read in HTML Data.mp4 | 51.3 MB | ||
| 6. Read in HTML Data.vtt | 11.1 KB | ||
| 6. Visualize Descriptive Statistics-Boxplots.mp4 | 11.5 MB | ||
| 6. Visualize Descriptive Statistics-Boxplots.vtt | 5.2 KB | ||
| 7. Broadcasting with Numpy.mp4 | 9 MB | ||
| 7. Broadcasting with Numpy.vtt | 3.8 KB | ||
| 7. Common Terms Relating to Descriptive Statistics.mp4 | 11.6 MB | ||
| 7. Common Terms Relating to Descriptive Statistics.vtt | 5.5 KB | ||
| 7. Conclusion to Section 4.mp4 | 5.4 MB | ||
| 7. Conclusion to Section 4.vtt | 2.2 KB | ||
| 7. Crosstabulation.mp4 | 10.9 MB | ||
| 7. Crosstabulation.vtt | 3.9 KB | ||
| 7. Hierarchical Clustering-theory.mp4 | 10.2 MB | ||
| 7. Hierarchical Clustering-theory.vtt | 5 KB | ||
| 7. Linear Regression-Implementation in Python.mp4 | 30.2 MB | ||
| 7. Linear Regression-Implementation in Python.vtt | 11.5 KB | ||
| 7. Pie Chart.mp4 | 12.8 MB | ||
| 7. Pie Chart.vtt | 5.6 KB | ||
| 7. SVM- Linear Classification.mp4 | 7.4 MB | ||
| 7. SVM- Linear Classification.vtt | 3.2 KB | ||
| 7. Some Miscellaneous IPython Usage Facts.mp4 | 12 MB | ||
| 7. Some Miscellaneous IPython Usage Facts.vtt | 4.5 KB | ||
| 7. Start With Deep Neural Network (DNN).html | 204.8 B | ||
| 8. Conditions of Linear Regression.mp4 | 3 MB | ||
| 8. Conditions of Linear Regression.vtt | 1.8 KB | ||
| 8. Data Distribution- Normal Distribution.mp4 | 9.6 MB | ||
| 8. Data Distribution- Normal Distribution.vtt | 3.9 KB | ||
| 8. Hierarchical Clustering-practical.mp4 | 29.4 MB | ||
| 8. Hierarchical Clustering-practical.vtt | 9.5 KB | ||
| 8. Line Chart.mp4 | 37.1 MB | ||
| 8. Line Chart.vtt | 12.1 KB | ||
| 8. Online iPython Interpreter.mp4 | 7.7 MB | ||
| 8. Online iPython Interpreter.vtt | 3.4 KB | ||
| 8. Reshaping.mp4 | 24.3 MB | ||
| 8. Reshaping.vtt | 9.6 KB | ||
| 8. SVM- Non Linear Classification.mp4 | 5.1 MB | ||
| 8. SVM- Non Linear Classification.vtt | 2.3 KB | ||
| 8. Solve Equations with Numpy.mp4 | 11.4 MB | ||
| 8. Solve Equations with Numpy.vtt | 4.2 KB | ||
| 8. Start with H20.mp4 | 12.1 MB | ||
| 8. Start with H20.vtt | 4.3 KB | ||
| 9. Check for Normal Distribution.mp4 | 16.5 MB | ||
| 9. Check for Normal Distribution.vtt | 5.6 KB | ||
| 9. Conclusion to Section 1.mp4 | 6.5 MB | ||
| 9. Conclusion to Section 1.vtt | 3.1 KB | ||
| 9. Conclusions to Section 6.mp4 | 5.8 MB | ||
| 9. Conclusions to Section 6.vtt | 2.2 KB | ||
| 9. Conditions of Linear Regression-Check in Python.mp4 | 33.4 MB | ||
| 9. Conditions of Linear Regression-Check in Python.vtt | 12.6 KB | ||
| 9. Default H2O Deep Learning Algorithm.mp4 | 8.2 MB | ||
| 9. Default H2O Deep Learning Algorithm.vtt | 3.4 KB | ||
| 9. Numpy for Statistical Operation.mp4 | 14.9 MB | ||
| 9. Numpy for Statistical Operation.vtt | 6.7 KB | ||
| 9. Pivoting.mp4 | 24 MB | ||
| 9. Pivoting.vtt | 8.4 KB | ||
| 9. Principal Component Analysis (PCA)-Theory.mp4 | 5.9 MB | ||
| 9. Principal Component Analysis (PCA)-Theory.vtt | 3 KB | ||
| 9. Support Vector Regression.mp4 | 10.2 MB | ||
| 9. Support Vector Regression.vtt | 4.3 KB | ||
| [Tutorialsplanet.NET].url | 102.4 B | ||
| ▲ 248 total files | |||
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