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Machine learning Training

Machine learning is a subfield of artificial intelligence that is concerned with the design, analysis, implementation, and applications of programs that learn from experience. It offers some of the most cost-effective approaches to automated knowledge acquisition in emerging data-rich disciplines (bioinformatics, cheminformatics, neuroinformatics, environmental informatics, social informatics, business informatics, security informatics, materials informatics, etc.). Learning algorithms can also be used to model aspects of human and animal learning.

Who should learn

Machine learning course is perfect for those who have a solid basis in R and statistics, but are complete beginners with machine learning. After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering.

Course outline

• 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)