Location
Location

Uppal Hyderabad

Location
Phone

+91 70934 77157

Machine Learning



Machine Learning

Overview of Machine learning

  • What is machine learning?
  • What are the use cases 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 Over fitting 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 neighbors, 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)