iClass Gyansetu

Course Curriculum

• Introduction to R Environment
• Data Handling (Importing and Exporting Data from Various Sources)
• Data Manipulation and Summariztion
• Data Exploration and Visualization
• Statistical Inference System (Statistics Analysis)
• Project Work
• Introduction to R Analytics
• Machine Learning using R
• Theory and Premise behind Machine Learning
• Defining a ML Problem
• The Target Function and the Loss Function
• Gradient Descent and Minimizing the Error
• Data Handling (Importing and Exporting Data from Various Sources)
• Data Manipulation and Summariztion
• Data Exploration and Visualization
• Statistical Inference System (Statistics Analysis)
• Deep Dive into Machine Learning
• Supervised Machine Learning
• Regression and Classification
• Support Vector Machines
• Naive Bayes
• Decision Tree 
• Random Forest
• Neural Networks
•  Unsupervised Machine Learning
• Clustering
• K – Means
• K – NN
• Case Study
• Predictive Analytics
• Tree Based Prediction
• Project Work