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  1. Introduction to Course
    1. What is this course about?
    2. How are we going to cover the content?
    3. Which tool are we going to use?
    4. What is unique about the course?
  2. What is Data Science Project and our methodology
    1. Project Management Techniques
    2. KDD
    3. CRISP-DM
  3. Working environment and Knime
    1. Installation of Knime
    2. Versioning of Knime
    3. Resources (Documentation, Forums and extra resources)
    4. Welcome Screen and working environment of Knime
  4. Welcome to Data Science
    1. Understanding of a workflow
    2. First end-to-end problem: Teaching to the machine
    3. First end-to-end workflow in Knime
  5. Understanding Problem
    1. Types of analytics
    2. Descriptive Analytics and some classical problems
    3. Predictive Analytics
    4. Prescriptive Analytics
  6. Understanding Data
    1. File Types
    2. Coloring Data
    3. Scatter Matrix
    4. Visualization and Histograms
  7. Data Preprocessing (Excel Files : cscon_gender ,  cscon_age)
    1. Row Filtering
    2. Rule Based Row Filtering
    3. Column Filtering
    4. Group by , Aggregate
    5. Join and Concatenation
    6. Missing Values and Imputation
    7. Date and Time operations
    8. Example 1
  8. Feature Engineering
    1. Encoding: One – To – Many
    2. Rule Engine
    3. Imbalanced Data: SubSampling, SMOTE
  9. Models
    1. Introduction to Machine Learning : Test and Train Datasets
    2. Introduction to Machine Learning: Problem Types
    3. Classification Problems (Excel Files : cscon_gender ) 
      1. Naive Bayes and Bayes Theorem
      2. Binning and Naive Bayes practicum (click to download the workflow)
      3. Decision Tree
      4. Decision Tree Practicum (. Click here to download the workflow  )
      5. K-Nearest Neighborhood
      6. KNN Practicum (click to download the workflow)
      7. Distance Metrics of KNN
      8. Distance Metrics Practicum (click here to download the knime workflow)
      9. Support Vector Machines
      10. Kernel Trick and SVM Kernels
      11. SVM Practicum
      12. End to End Practicum for Classification
      13. Extra: Logistic Regression
      14. Extra: Logistic Regression Practicum
    4. ARM Problems
      1. ARM / ARL Concept
      2. A priori algorithm and association rule extraction
      3. ARM Practicum
    5. Clustering Problems
      1. Introduction to Clustering Concept
      2. K-Means
      3. Optimum K Value in k-Means
      4. K-Means Practicum
      5. Grid Search for optimum k value in k-means
      6. Hierarchical Clustering (Divisive and Agglomerative Approaches)
      7. HC Practicum
      8. DBSCAN
      9. DBSCAN Practicum
    6. Regression Problems
      1. Linear Regression
      2. Linear Regression Practicum
      3. Evaluation of Prediction Modes
      4. Practicum of Evaluation
      5. Multiple Linear Regression
      6. Multiple Linear Regression Practicum
      7. Polynomial Regression
      8. Polynomial Regression Practicum
      9. Simple Regression Tree
      10. Simple Regression Tree Practicum
      11. Example 2: Stock market prediction
  10. Knime as a tool : Some Advanced Operations
    1. PMML File Types and saving the model
    2. PMML Practicum with Knime
    3. MetaNodes
    4. Variables and Flow of a variable
    5. Loops and optimizing the model parameters
  11. Evaluation
    1. Introduction to Evaluation
    2. ZeroR Algorithm, Imbalanced Data Set and Baseline
    3. k-fold Cross Validation
    4. Confusion Matrix, Precision, Recall, Sensitivity, Specificity
    5. Evaluation of clustering: purity , randindex
    6. Evaluation of prediction: rmse, rmae, mse, mae
    7. Evaluation Practicum with knime: Example 3
    8. Evaluation of ARM
  12. Reporting 
    1. Exporting Reports to Images (Data to Report)
  13. Connecting Knime with other Languages
    1. Java Snippet
    2. R Snippet
    3. Python Snippet
  14. Meta Learners
    1. Ensemble Techniques: Bagging, Boosting and Fusion
    2. Random Forest ensemble learning technique for Classification
    3. Random Forest ensemble learning technique for Regression
    4. Random Forest Practicum
    5. Gradient Boosted Tree Regression
    6. Gradient Boosted Tree Regression Practicum
    7. Example 4
  15. Deep Learning
    1. Introduction to artificial neural networks
    2. Linearly Separable Problems and beyond
    3. DL4J Extension
  16. Real Life Practicums
    1. Resources about real life applications : Job Search, Forums, Competitions etc.
    2. Predicting the customer will pay or not
    3. Predicting the period of payments
    4. Credit Limit
    5. Customer Segmentation
  17. Bonus
    1. Loading different train and test datasets
    2. Data Preprocessing Practicum(Click to download knime file)
    3. Regression Practicum  (Simple Linear, Multiple Linear Regression, Correlation Matrix, p-Value and Feature Elimination (backward Elimination, Forward Selection) )Knime Files: File 1File 2
    4. Comparing the Regression Models : Decision Tree Regression, Random Forest Regression, Linear Regression, Polynomial Regression (Click to Download the Knime File)
    5. Evaluation of Regression Models (R2 and adjusted R2 ), Introduction to Classification problems and Logistic Regression
    6. Time Series Analysis and Classification algorithms : Decision Tree, Random Forest
    7. Imbalanced Datasets
    8. Customer Segmentation and Python Click to Download Knime File , Click to Download Dataset)
    9. Comparison of Clustering Algorithms: K-Means, K-Medoids ve Hierarchical Clustering (HC) and finding optimum Number of clusters with WCSS for K-Means ( cluster distance functions min, Max, group average, center, ward’s method) : Click to download knime file
      • Steps for above workflow
      • 1. Load Iris dataset
        1.1. Filter the class column
        2. For K-Means and K-Medoids
        2.1. Find the best cluster number
        2.2. Cluster with K-means and K-medoids
        3. Find the best K value for Hierarchical Clustering(HC)
        4. Cluster with HC
        5. Cluster with K=3
        6. Compare your results with K=3
        7. Report the best clustering
      • Evaluations:HC :  24 Error
        KMeans : 17 error
        KMedoids : 16 error
    10. Text Mining