- Data Science with R
- Introduction to Machine Learning
- Random Forest
- General Boosting & Bagging
- Support Vector Machines
- Neural Networks
- Text Mining with R
1. Data Science with R
- Exploratory Data Analysis and Visualization
- R for Data Science
- Data Mining
- Data Analysis for Evidence Based Decision Making
- Industry Applications of Advanced Analytics Models
- Big Data Analytics with Spark
- Project Management in Analytics
- Information to Insight
- Career Management
2. Introduction to Machine Learning
- An Introduction
- The Regression Algorithms
- The Classifiers: Bayesian and kNN
- Tree Based Algorithms
- SVM and Improving Performance
3. Random Forest
- Single Decision Tree
- Rise of Ensemble Method
- Practical Exercises in R on Business Case Studies
4. General Boosting & Bagging
- Decision Tree Ensembles: Bagging and Boosting
- The Case Study: Analysis of Credit Data
- The Case Study: The Titanic Accident
- The Case Study: Comparing Algorithms
5. Support Vector Machines
- Introduction to the Support Vector Machines
6. Neural Networks
- An Introduction
- The Perceptron learning procedure
- The backpropagation learning procedure
- Learning feature vectors for words
- Object recognition with neural nets
- Optimization: How to make the learning go faster
- Recurrent neural networks
- More recurrent neural networks
- Ways to make neural networks generalize better
- Combining multiple neural networks to improve generalization
- Hopfield nets and Boltzmann machines
- Restricted Boltzmann machines (RBMs)
- Stacking RBMs to make Deep Belief Nets
- Deep neural nets with generative pre-training
- Modeling hierarchical structure with neural nets
- Recent applications of deep neural nets
7. Text Mining with R
- An Introduction to the Text Mining
- TM Packages in R
- Regular Expressions
- Sentiment Analysis
- Topic Modelling
- Network Analysis
- Clustering
Note: to know about the detailed information about the course modules please feel free to write us or give us a buzz.