Advanced Data Analytics and Machine Learning with R


Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Many industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data, organizations are able to work more efficiently or gain an advantage over competitors.

To get the most value from machine learning, this 3 days course on Machine Learning will teach you most of the key machine learning using R. R is particularly suited to learning machine learning due to user friendliness and the powerful RStudio IDE.

Course Objectives:

  • What is machine learning
  • Supervised Learning Models
  • Unsupervised Learning Models
  • Neural Network


15 hours, 2 Days Course

Mode of Delivery

Classroom-based, Instructor-led Training


Course Outlines

  1. Introduction to Machine Learning
    1. What is Machine Learning
    2. Types of Machine Learning
    3. Supervised vs Unsupervised Learning
    4. Python vs R for Machine Learning
    5. Install R Machine Learning Package
  2. Data Preprocessing
    1. Sample Data
    2. Impute Missing Data
    3. Normalize Data
    4. Split Data
  3. Regression Methods
    1. What is Linear Regression
    2. Regularization – Bias vs Variance Tradeoff
    3. Lasso Regression
    4. Ridge Regression
  4. Classification Methods
    1. What is Classification
    2. Logistic Regression
    3. Gaussian Naive Bayes (GNB)
    4. K Nearest Neighbor (KNN)
    5. Support Vector Machine (SVM)
    6. Decision Tree
    7. Confusion Matrix
    8. ROC and AUC Analysis
  5. Clustering Methods
    1. Distance Measure
    2. K Means Clustering
    3. Hierarchical Clustering
    4. Silhouette Analysis
  6. Ensemble Methods
    1. Types of Ensemble Methods
    2. Random Forest Ensemble
    3. Gradient Boost and XGBoost Ensemble
    4. Stacking Ensemble
  7. Hyperparameter Tuning
    1. Exhaustive Grid Search
    2. Random Search
  8. Neural Network
    1. What is Neural Network
    2. Activation Functions
    3. Deep Learning vs Machine Learning
    4. Classification Using Neural Network