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DAT-610

(SNHU-DAT610.AA1)
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Skills You’ll Get

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1

Intro

  • Analyzing Stock Market Trends Using the Smarket Dataset
  • Analyzing the Wage Dataset
  • Implementing RNN for Time Series Prediction
  • Creating an Image Classifier Using CNNs
  • Implementing FDR
  • Implementing the BH Procedure
  • Implementing Holm's Step-Down Procedure
  • Implementing FWER
  • Analyzing the NCI60 Dataset
  • Implementing K-Means Clustering
  • Incorporating Shrinkage Techniques into the Cox Model
  • Implementing a Dendrogram
  • Applying the Log-Rank Test
  • Implementing the Kaplan-Meier Survival Curve
  • Implementing SVM with Multiple Classes
  • Implementing SVC
  • Creating and Analyzing an ROC Curve
  • Implementing the Maximal Margin Classifier
  • Implementing KNN on the Caravan Dataset
  • Implementing Poisson Regression
  • Implementing GLM
  • Implementing LDA
  • Building and Analyzing a Classification Tree Using the Carseats Dataset
  • Improving Model Performance Using Boosting
  • Implementing Bagging and Random Forests
  • Fitting Regression Trees
  • Improving GAM
  • Implementing Splines
  • Implementing a Step Function
  • Implementing Polynomial Regression
  • Improving Predictions with PCR
  • Generating and Visualizing a Multivariate Gaussian Distribution
  • Implementing PLS
  • Implementing Ridge Regression
  • Implementing Lasso Regression
  • Implementing QDA
  • Implementing Forward and Backward Stepwise Selection
  • Implementing Subset Selection Methods Using the Hitters Dataset
  • Implementing Naive Bayes Classification
  • Implementing Multiple Logistic Regression
  • Implementing Multinomial Logistic Regression
  • Implementing Bootstrapping Techniques on the Portfolio Dataset
  • Implementing Non-Linear Transformations of Predictors
  • Implementing K-Fold Cross-Validation
  • Implementing Qualitative Predictors Using the Credit Dataset
  • Performing Multiple Linear Regression
  • Implementing LOOCV
  • Implementing the Validation Set Approach
  • Implementing Simple Linear Regression
  • Indexing the Data
  • Implementing the Bayes Classifier
  • Implementing the Bias-Variance Trade-Off

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