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
Generalization & Non Linearity
Recursive Partitioning(Decision Trees)
Ensemble Models(Random Forest, Bagging & Boosting(ada, gbm etc))
Artificial Neural Networks(ANN)
Support Vector Machines(SVM)
Challenges of unsupervised learning and beyond K-means
Market Basket Analysis
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
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)
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