Machine Learning Specialist Training

• Develop an understanding of categorical variables and continuous variables, that helps in using the boosting and bagging methods effectively, understanding the NN algorithms work.
• Understand kernel functions such as: spline kernels, linear, radial basis function and polynomial and Text Mining with R are based on the intelligence of statistics and R.
• Explore R language fundamentals, including basic syntax, variables, and types
• Why Support Vector Machines  is called the most high-performing algorithm
• How neural networks effective in image segmentation
• How to use the calculus in simpler form

• The IT professionals, who are willing to pursue their career as a Machine Learning Specialist.

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.