Data Analytics for Machine Learning with R
IIT (ISM) 6 day (48 hours) Full Time, Lab Oriented PROFESSIONAL DEVELOPMENT PROGRAM
This IIT Certified Intensive Lab Oriented Course
is focused on building industry ready Data
Scientist who can work on machine learning,
data mining, and statistical modelling for
predictive and prescriptive enterprise analytics.
This program will enable you to develop deep
understanding of and experience with machine
learning and data analysis. Familiarity with
common tools for data management and
analysis including machine learning can be
applied on real world problems for building
predictive models using machine learning on
R is the most popular data analytics tool owing to it being open-source, its flexibility, packages and community. “R” wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
On completion of the program, students will have developed a world-class skillset in their selected technology domain that provides “Employability Enhancing” skills and capabilities thereby substantially increasing their earning potential and compensation benchmarks. All enrolled participant’s will be provided access to other learning aids, reference materials, assessments and hands on workshops as appropriate. During the course students will also be allocated Project work that is designed to provide adequate practical and hands on experience in implementing the concepts learned during the course.
What you will learn from this course
- Introduction to Artificial Intelligence (Evolution of Technology)
- Branches of Artificial Intelligence and what is Machine Learning.
- Supervised, Unsupervised & Reinforcement Learning.
- How machine learning can be applied in technology, science, trading etc.
- Comparison B/W R, Python & SAS
- Why Learn R?
- Introduction to R.
- R Overview, R Interface, R Work Space, Help, Variables, Programming
- Install R.
- Running a few simple programs
- Some Common Terms & Basics in R
- Data Types
- Importing Data
- Keyboard Input, Database Input, Export Data
- Variable Labels, Value Labels, Missing Data, date Values
- R Iteration & Conditional Constructs
- R Packages: installation and Usages
- Data Manipulation
- Hands On Session
- Some Advance Programs using Data from R Data repository
- Architecture of CNN
- Types of layers in CNN
- Building an image classifier using CNN
- Deep Learning with CNN
- Introduction to Data Visualization
- Basic Graphics: line, bar, box, histogram plots
- Scatter plots
- Basic Statistics: mean median, mode, percentile, quantile
- Frequency Distribution, Histogram Analysis
- Data: Distribution, Types of Data Distribution and Hypothesis Testing
- Introduction to Predictive Models
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- Implementation of Predictive Models using R
- The Art of Feature Engineering, Pattern recognition & Principal component analysis
? ? ?
- Classification & Clustering
- Supervised Learning K- Nearest Neighbors Classification
- Unsupervised Learning K – means Clustering Algorithm
- Reinforcement Learning
- Implementation of Classification & Clustering Using R
- Introduction to Neural Networks
- Introduction Deep Learning
- Implementation of Neural Network using R, ? Multi layer Perceptron (MLP) ? Support Vector Machine (SOM)