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11. Section 3 Quiz.html
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11. knn-Regression.mp4
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12. Gradient Boosting-classification.mp4
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12. Logistic Regression.mp4
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14. Section 8 Quiz.html
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14. Voting Classifier.mp4
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15. Conclusions to Section 11.mp4
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2. Create Numpy Arrays.mp4
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2. Data Preparation for Supervised Learning.mp4
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2. Different Types of Data Used in Statistical & ML Analysis.mp4
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2. Introduction to the Course & Instructor.mp4
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2. KMeans-theory.mp4
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2. Perceptrons for Binary Classification.mp4
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2. Read in Data from Online CSV.mp4
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2. Removing NAsNo Values From Our Data.mp4
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2. Some Pointers on Collecting Data for Statistical Studies.mp4
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2. Test the Difference Between Two Groups.mp4
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2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4
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3. Basic Data Handling Starting with Conditional Data Selection.mp4
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3. Data For the Course.html
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3. Different Types of Data Used Programatically.mp4
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3. Getting Started with ANN-binary classification.mp4
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3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4
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3. KMeans-implementation on the iris data.mp4
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3. Numpy Operations.mp4
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3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4
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3. Read Data from a Database.mp4
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3. Read in CSV Data Using Pandas.mp4
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3. Some Pointers on Exploring Quantitative Data.html
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3. Test the Difference Between More Than Two Groups.mp4
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3.1 scriptsLecture.zip.zip
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4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4
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4. Drop ColumnRow.mp4
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4. Explore the Quantitative Data Descriptive Statistics.mp4
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4. Explore the Relationship Between Two Quantitative Variables.mp4
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4. Introduction to the Python Data Science Tool.mp4
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4. Matrix Arithmetic and Linear Systems.mp4
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4. Multi-label classification with MLP.mp4
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4. Naive Bayes Classification.mp4
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4. Python Data Science Packages To Be Used.mp4
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4. Quantifying KMeans Clustering Performance.mp4
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4. Read in Excel Data Using Pandas.mp4
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4. Using Logistic Regression as a Classification Model.mp4
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5. Conclusions to Section 2.mp4
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5. Correlation Analysis.mp4
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5. Data Imputation.mp4
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5. For Mac Users.mp4
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5. Grouping & Summarizing Data by Categories.mp4
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5. KMeans Clustering with Real Data.mp4
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5. Numpy for Basic Vector Arithmetric.mp4
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5. RF-Classification.mp4
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5. Reading in JSON Data.mp4
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5. Regression with MLP.mp4
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5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4
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5. Subset and Index Data.mp4
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6. Barplot.mp4
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6. Basic Data Grouping Based on Qualitative Attributes.mp4
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6. How Do We Select the Number of Clusters.mp4
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6. Introduction to the Python Data Science Environment.mp4
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6. Linear Regression-Theory.mp4
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6. MLP with PCA on a Large Dataset.mp4
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6. Numpy for Basic Matrix Arithmetic.mp4
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6. RF-Regression.mp4
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6. Read in HTML Data.mp4
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6. Visualize Descriptive Statistics-Boxplots.mp4
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7. Broadcasting with Numpy.mp4
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7. Common Terms Relating to Descriptive Statistics.mp4
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7. Conclusion to Section 4.mp4
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7. Crosstabulation.mp4
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7. Hierarchical Clustering-theory.mp4
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7. Linear Regression-Implementation in Python.mp4
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7. Pie Chart.mp4
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7. SVM- Linear Classification.mp4
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7. Some Miscellaneous IPython Usage Facts.mp4
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8. Conditions of Linear Regression.mp4
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8. Data Distribution- Normal Distribution.mp4
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8. Hierarchical Clustering-practical.mp4
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MP4
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29.4 MB
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8. Hierarchical Clustering-practical.vtt
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VTT
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9.5 KB
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8. Line Chart.mp4
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MP4
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37.1 MB
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8. Line Chart.vtt
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VTT
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12.1 KB
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8. Online iPython Interpreter.mp4
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MP4
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7.7 MB
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8. Online iPython Interpreter.vtt
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VTT
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3.4 KB
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8. Reshaping.mp4
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MP4
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24.3 MB
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8. Reshaping.vtt
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VTT
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9.6 KB
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8. SVM- Non Linear Classification.mp4
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MP4
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5.1 MB
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8. SVM- Non Linear Classification.vtt
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VTT
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2.3 KB
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8. Solve Equations with Numpy.mp4
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MP4
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11.4 MB
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8. Solve Equations with Numpy.vtt
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VTT
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4.2 KB
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8. Start with H20.mp4
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MP4
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12.1 MB
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8. Start with H20.vtt
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VTT
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4.3 KB
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9. Check for Normal Distribution.mp4
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MP4
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16.5 MB
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9. Check for Normal Distribution.vtt
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VTT
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5.6 KB
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9. Conclusion to Section 1.mp4
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MP4
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6.5 MB
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9. Conclusion to Section 1.vtt
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VTT
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3.1 KB
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9. Conclusions to Section 6.mp4
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MP4
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5.8 MB
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9. Conclusions to Section 6.vtt
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VTT
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2.2 KB
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9. Conditions of Linear Regression-Check in Python.mp4
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MP4
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33.4 MB
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9. Conditions of Linear Regression-Check in Python.vtt
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VTT
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12.6 KB
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9. Default H2O Deep Learning Algorithm.mp4
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MP4
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8.2 MB
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9. Default H2O Deep Learning Algorithm.vtt
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VTT
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3.4 KB
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9. Numpy for Statistical Operation.mp4
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MP4
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14.9 MB
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9. Numpy for Statistical Operation.vtt
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VTT
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6.7 KB
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9. Pivoting.mp4
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MP4
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24 MB
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9. Pivoting.vtt
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VTT
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8.4 KB
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9. Principal Component Analysis (PCA)-Theory.mp4
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MP4
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5.9 MB
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9. Principal Component Analysis (PCA)-Theory.vtt
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VTT
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3 KB
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9. Support Vector Regression.mp4
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MP4
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10.2 MB
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9. Support Vector Regression.vtt
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VTT
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4.3 KB
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[Tutorialsplanet.NET].url
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URL
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102.4 B
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![Udemy - Complete Data Science Training with Python for Data Analysis [TP]](/static/img/no-torrent-image.png "Udemy - Complete Data Science Training with Python for Data Analysis [TP]")
