Random Forest Algorithm in Machine Learning

Through this training we are going to learn and apply how the random forest algorithm works

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training, we are going to learn and apply how the random forest algorithm works and several other important things about it. The course includes the following;

What you’ll learn

  • Learn to extract the Data to the platform and Apply data Transformation. Also learn the bifurcation of Data.
  • Build a Random Forest Model on Training Data set and Predict using Testing Data set..
  • You will be learning to Validate the Model Performance, Improve the model Performance using Random Forest, Predict and Validate Performance of Model..

Course Content

  • Introduction –> 1 lecture • 10min.
  • Getting Started –> 6 lectures • 37min.
  • Node Value and Subsample –> 6 lectures • 29min.

Random Forest Algorithm in Machine Learning

Requirements

  • Students should have enough familiarity of basic linear algebra, calculus, probability and statistic. These courses need not be at a very high level. If you remember what you learnt in high school or junior college or can revise it quickly, then that should be enough..
  • Familiarity with at least one programming language is recommended. Anyone language such as C, C++, Java, PHP etc. are fine. This ensure that you understand the programming examples and assignment and does not spend too much time there. If you have not done coding before, you can take a bridge course before enrolling for this machine learning training. This will make your life very easy..

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training, we are going to learn and apply how the random forest algorithm works and several other important things about it. The course includes the following;

1) Extract the Data to the platform.

2) Apply data Transformation.

3) Bifurcate Data into Training and Testing Data set.

4) Built Random Forest Model on Training Data set.

5) Predict using Testing Data set.

6) Validate the Model Performance.

7) Improve the model Performance using Random Forest.

8) Predict and Validate Performance of Model.

In a world where we generate 2.5 quintillion bytes (1 quintillion bytes = 1018 bytes!) every day, it becomes important for people who can read and derive meaning from that data. This course helps you be that person who can derive meaning out of this huge data with organizations. During this course, we would take you through different concepts. One can master the concepts taught during this course so that it runs in the blood of the programmer and gets easygoing for him to apply them in real-life situations. These topics when taught will boost up the confidence and the projects along with the course will add to push that confidence beyond 100%.