Overview

Best Machine Learning course for aspirants or professionals with prior knowledge of R and want to earn Machine Learning certification. This course imparts practical hands-on knowledge of Machine Learning with R and covers most widely used supervised and unsupervised Machine Learning techniques, including social media text analytics and recommendation engine.

For aspirants who want to learn Machine Learning training but have no prior knowledge of Analytics and R, please refer to the following courses before proceeding with this course:

• Data Science using R
• Basic/Advance Data Science using Python

Considering the cost effectiveness and real-life application based curriculum, this is the best Machine Learning training course for Analytics professional (with working knowledge of R) who are specifically looking for an industry relevant Machine Learning certification.

Duration

40 hours

 Intended Audience

Analytics professionals or aspirants with prior working knowledge of R, who are looking Machine Learning certification to up-skill with practical application of Machine Learning with R.

Course outlines:

1.      Introduction to Machine learning

• 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

2.      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

3.      Unsupervised Learning

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

4.      Recommendation Engine

• Market Basket Analysis
• Collaborative Filtering

5.      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

6.      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)