R Machine Learning Training Chennai – Fasttrack Weekdays / Weekends Training
During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science.
Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn.
R is rapidly becoming the leading language in data science and statistics. Today, the R programming language is the tool of choice for data scientists in every industry and field. Whether you are a full-time number cruncher, or just the occasional data analyst, R will suit your needs.
- Predict the outcomes of elections
- Identify and filter spam messages from e-mail
- Foresee criminal activity
- Automate traffic signals according to road conditions
- Produce financial estimates of storms and natural disasters
- Examine customer churn
- Create auto-piloting planes and auto-driving cars
- Identify individuals with the capacity to donate
- Target advertising to specific types of consumers
For now, don’t worry about exactly how the machines learn to perform these tasks; we will get into the specifics later. But across each of these contexts, the process is the same.
- Data input: It utilizes observation, memory storage, and recall to provide a factual basis for further reasoning.
- Abstraction: It involves the translation of data into broader representations.
- Generalization: It uses abstracted data to form a basis for action.
- Collecting data: Whether the data is written on paper, recorded in text files and spreadsheets, or stored in an SQL database, you will need to gather it in an electronic format suitable for analysis.
- Exploring and preparing the data: The quality of any machine learning project is based largely on the quality of data it uses. This step in the machine learning process tends to require a great deal of human intervention.
- Training a model on the data: By the time the data has been prepared for analysis, you are likely to have a sense of what you are hoping to learn from the data
- Evaluating model performance: Because each machine learning model results in a biased solution to the learning problem, it is important to evaluate how well the algorithm learned from its experience.
- Improving model performance: If better performance is needed, it becomes necessary to utilize more advanced strategies to augment the performance of the model. Sometimes, it may be necessary to switch to a different type of model altogether.
- A football team will win or lose
- A person will live past the age of 100
- An applicant will default on a loan
- An earthquake will strike next year