Data Science in R ( Explore Predictive Modelling , Machine Learning and Deep Learning) - Dvanalytics
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Data Science in R ( Explore Predictive Modelling , Machine Learning and Deep Learning)

0 STUDENTS ENROLLED

    Course Curriculum

    Session - 1
    Introduction to R 00:00:00
    <ul> <li>Installation procedure</li> <li> Getting Started in R</li> <li>R Environment • R Packages</li> <li>R Data Types Vectors</li> <li>R Dataframes</li> <li>List in R</li> <li>Factor and Matrices</li> <li>R History and Scripts</li> <li>R Functions</li> <li>Errors in R</li> </ul>
    Data Handling in R 00:00:00
    <ul> <li>Data handling introduction</li> <li>Importing the Datasets</li> <li>Checklist</li> <li>Subsetting the Data</li> <li>Subsetting Variable Condition</li> <li>Calculated Fields _ ifelse</li> <li>Sorting and Duplicates</li> <li>Joining and Merging</li> <li>Exporting the Data</li> </ul>
    Basic Descriptive Statistics & Reporting 00:00:00
    <ul> <li>Basic Statistics, Plots and Reporting in R</li> <li>Introduction and Sampling</li> <li>Descriptive Statistics</li> <li>Percentiles and Quartiles</li> <li>Box Plots</li> <li>Creating Graphs and Conclusion</li> </ul>
    Session - 2
    Data Cleaning and Treatment in R 00:00:00
    <ul> <li>Data Cleaning Intro and Model Building Cycle</li> <li>Model Building Cycle</li> <li>Data Cleaning Case Study</li> <li>CS lab Step1 Basic Content of Dataset</li> <li>Variable Level Exploration Categorical</li> <li>Reading Data Dictionary</li> <li>Step2 lab Categorical Variable Exploration</li> <li>Step3 lab Variable level Exploration – Continuous</li> <li>Data Cleaning and Treatments</li> <li>Step 4 Treatment – Scenario 1</li> <li>Step 4 Treatment – Scenario 2</li> <li>Data Cleaning – Scenario 3</li> </ul>
    00:00
    Session - 3
    Logistic Regression using R 00:00:00
    <ul> <li>Logistic Regression in R</li> <li>Need of Non-Linear Regression</li> <li>Logistic Function and Line</li> <li>Multiple Logistic Regression</li> <li>Goodness of Fit for a Logistic Regression</li> <li>Multicollinearity in Logistic Regression in R</li> <li>Individual Impact of Variables in R</li> <li>Model Selection in R</li> <li>Logistic Regression Conclusion</li> </ul>
    Decision Tree in R 00:00:00
    <ul> <li>Handout – Decision Tree in R</li> <li>Introduction to Decision Tree & Segmentation</li> <li>The Decision Tree Philosophy & The Decision Tree Approach</li> <li>The Splitting criterion &Entropy Calculation</li> <li>Information Gain & Calculation</li> <li>The Decision tree Algorithm</li> <li>Split for Variable & The Decision tree-lab(Part 1)</li> <li>The Decision tree-lab(Part 2) & Validation</li> <li>The Decision tree -lab (Part3) & Overfitting</li> <li>Pruning & Complexity Parameters</li> <li>Choosing Cp & Cross Validation Error</li> <li>Two types of Pruning</li> <li>Tree Building & Model Selection-Lab</li> </ul>
    Session - 4
    Model Selection and Cross validation 00:00:00
    <ul> <li>How to validate a model?</li> <li>What is a best model?</li> <li>Types of data</li> <li>Types of errors</li> <li>The problem of over fitting</li> <li>The problem of under fitting</li> <li>Bias Variance Trade-off</li> <li>Cross validation</li> <li>Boot strapping</li> <li>Attrition data case study</li> </ul>
    Neural Networks 00:00:00
    <ul> <li>Neural network Intuition</li> <li>Neural network and vocabulary</li> <li>Neural network algorithm</li> <li>Math behind neural network algorithm</li> <li>Building the neural networks</li> <li>Validating the neural network model</li> <li>Neural network applications</li> <li>Image recognition using neural networks</li> </ul>
    Session - 5
    Support Vector Machine – SVM 00:00:00
    <ul> <li><em>Support Vector Machine</em></li> <li><em>Introduction To SVM</em></li> <li><em> The Classifier and Decision Boundary</em></li> <li><em>SVM – The Large Margin Classifier</em></li> <li><em> The SVM Algorithms and Results</em></li> <li><em>SVM on R</em></li> <li><em> Non Linear Boundary</em></li> <li><em>Kernal Tric </em></li> <li><em>Kernal Trick on R </em></li> <li><em>Soft Margin and Validation</em></li> <li><em> SVM Advantages, Disadvantages and Applications</em></li> <li><em>Lab Digit recognize </em></li> <li><em>SVM Conclusion</em></li> </ul>
    Random Forest and Boosting 00:00:00
    <ul> <li>Introduction</li> <li>Ensemble Learning</li> <li>How ensemble learning works</li> <li>Bagging Building models using</li> <li>Bagging Random Forest algorithm</li> <li>Random Forest model building</li> <li>Finetuning parameters and model selection</li> <li>Boosting Introduction</li> <li>Boosting algorithm</li> <li>GBM Model building and Validating in python</li> </ul>
    Session - 6
    Cluster Analysis 00:00:00
    <ul> <li>Handout – Cluster Analysis</li> <li>Introduction to Clustering via Segmentation</li> <li>Types of Clusters</li> <li>Similarities and Dissimilarity</li> <li>Calculating the Distance</li> <li>Calculating Distance in R</li> <li>Clustering Algorithms – Kmeans</li> <li>Kmeans Clustering on R</li> <li>More on Kmeans</li> <li>Data Standardisation and Non-numeric Data</li> </ul>
    Machine Learning Projects using R 00:00:00
    <ol> <li>Consumer Loan Default Prediction</li> <li>Bank Tele Marketing</li> <li>Automobile Pricing Strategy</li> <li>Census Income</li> <li>Direct Mail Marketing</li> <li>Credit Card Ratings</li> </ol>