•  Introduction to logistic regression
•  Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
•  Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
•  Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
•  Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
•  Real-life applications of logistic regression
• Hands-on Exercise:
•  Implementing predictive analytics by describing data
•  Explaining the relationship between one dependent binary variable and one or more binary variables
•  Using glm() to build a model, with ‘Churn’ as the dependent variable

•  What is classification? Different classification techniques
•  Introduction to decision trees
•  Algorithm for decision tree induction and building a decision tree in R
•  Confusion matrix and regression trees vs classification trees
•  Introduction to bagging
•  Random forest and implementing it in R
•  What is Naive Bayes? Computing probabilities

 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node

•  Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
• Hands-on Exercise:
•  Implementing random forest for both regression and classification problems
•  Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
•  Using ROCR for performance metrics

•  What is Clustering? Its use cases
•  what is k-means clustering? What is canopy clustering?
•  What is hierarchical clustering?
•  Introduction to unsupervised learning
•  Feature extraction, clustering algorithms, and the k-means clustering algorithm
•  Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
•  Dendograms, understanding hierarchical clustering, and implementing it in R
•  Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
• Hands-on Exercise:
•  Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
•  K-means clustering for visualizing and interpreting results for the customer churn data

• 1 Introduction to association rule mining and MBA
•  Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
•  Introduction to recommendation engines
•  User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
•  Recommendation engine use cases
• Hands-on Exercise:
•  Deploying association analysis as a rule-based Machine Learning method
•  Identifying strong rules discovered in databases with measures based on interesting discoveries

•  Introducing Artificial Intelligence and Deep Learning
•  What is an artificial neural network? TensorFlow: The computational framework for building AI models
•  Fundamentals of building ANN using TensorFlow and working with TensorFlow in R

•  What is a time series? The techniques, applications, and components of time series
•  Moving average, smoothing techniques, and exponential smoothing
•  Univariate time series models and multivariate time series analysis
•  ARIMA model
•  Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
• Hands-on Exercise:
•  Analyzing time series data
•  Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series

•  Introduction to Support Vector Machine (SVM)
•  Data classification using SVM
•  SVM algorithms using separable and inseparable cases
•  Linear SVM for identifying margin hyperplane

•  What is the Bayes theorem?
•  What is Naïve Bayes Classifier?
•  Classification Workflow
•  How Naive Bayes classifier works and classifier building in Scikit-Learn
•  Building a probabilistic classification model using Naïve Bayes and the zero probability problem

•  Introduction to the concepts of text mining
•  Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
•  Text mining algorithms and the quantification of the text
•  TF-IDF and after TF-IDF