• What is machine learning?
• What are the use case of Machine learning?
• Statistical learning vs. Machine learning
• Iteration and evaluation
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
• Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
• Introduction to CARET package
• Introduction to H2O package

SUPERVISED LEARNING

• Linear Regression
• Logistic regression
• Generalization & Non Linearity
• Recursive Partitioning(Decision Trees)
• Ensemble Models(Random Forest, Bagging & Boosting(ada, gbm etc))
• Artificial Neural Networks(ANN)
• Support Vector Machines(SVM)
• K-Nearest neighbours
• Naive Bayes

UNSUPERVISED LEARNING

• K-means clustering
• Challenges of unsupervised learning and beyond K-means

RECOMMENDATION ENGINE

• Market Basket Analysis
• Collaborative Filtering

SOCIAL MEDIA AND TEXT ANALYTICS USING R

• Social Media – Characteristics of Social Media
• Applications of Social Media Analytics
• Metrics(Measures Actions) in social media analytics
• Examples & Actionable Insights using Social Media Analytics
• Text Analytics – Sentiment Analysis using R
• Text Analytics – Word cloud analysis using R
• Text Analytics - K-Means Clustering

TEXT MINING, SOCIAL NETWORK ANALYSIS AND NLP

• Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Vector space models; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
• Handling big graphs
• The purpose of it all: Finding patterns in data
• Finding patterns in text: text mining, text as a graph
• Natural Language processing (NLP)