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SL NOCLASS NAMEDURATIONDESCRIPTION
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1Chapter 1 Class 1 60R basics, New connection, Values, Data structures, Variable, Vector, Indexing
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2Chapter 1 Class 2 85Replicate elements of vectors and lists, Data Frames, Combine R objects by rows or columns, Matrices, Print values, Concatenate strings,
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3Chapter 1 Class 347Basic Plotting Function
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4Chapter 2 and Chapter 3 Class 1 57Confidence intervals for model parameters, Predict method for linear model fits,
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5Chapter 2 and Chapter 3 Class 2 68F test, Create a new ggplot,
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6Chapter 2 and Chapter 3 Class 3 47Ggplot graph, Prob density function, Boxplots,
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7Chapter 2 and Chapter 3 Class 4 58Model building process, Data collection and validation, Exploratory data analysis,
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8Chapter 2 and Chapter 3 Class 5 48Bias variance trade off,
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9Chapter 2 and Chapter 3 Class 6 55Coefficients,
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10Chapter 4 Class 1 34Construct design matrics, Predict Method for GLM Fits,
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11Chapter 4 Class 2 51Predict Method for GLM Fits, Deviance, Saturated model, Analysis of deviance for general, Data splitting functions,
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12Chapter 4 Class 3 74Q4.8, Clean our data, Converting integers into where factors, Analysing the data, Categorical analysis, Reducing number of levels,
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13Chapter 4 Class 4 59Apply a function over a list or vector, Family objects for models, Create a training dataser, Tidyverse, Confusion matrix, Choose a model by AIC in a stepwise Algorithm,
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14Chapter 5 Class 1 5444 Case study 3, GLMs for count and aggregate loss variables, Chap5 Tree based models,
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15Chapter 5 Class 2 54Q5.6, Q5.9, Q5.10, Q5.11,
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16Chapter 5 Class 3 51Creating a wage flag, Data analysis for wages,
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17Chapter 6 Class 1 72 UNSUPERVISED LEARNING, Principal Component Analysis:Conceptual Foundation;Additional PCA ideas,Drawbacks of PCA
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18Chapter 6 Class 2 97PCA-Simple Case Study, Example 6.1.7, 6.1.8,6.1.10,Cluster Analysis,,K-means Clustering
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