Start your path to becoming a machine learning expert! Let the curtains of machine learning and Random Forest be lifted. Explore a state-of-the-art algorithm in detail with practical implementation using Random Forest and Python
Key Features
- Use the power of Python to train your machine to learn like a human and make predictions!
- Learn data preprocessing steps to prepare data for machine learning algorithms
- Master machine learning concepts and implement the essential ML algorithm, Random Forest
Book Description
Machine learning is designed to understand and build methods that 'learn' to leverage data to improve performance on a set of tasks. Machine learning algorithms are used in a plethora of applications in medicine, email filtering, speech recognition, and more, where it is challenging to develop conventional algorithms to perform tasks.
The course begins with an introduction to machine learning concepts and explains the motivation for machine learning. The course teaches all major concepts about Python including variables, objects, strings, loops, decision-making statements, classes, and a small project to recap. You will learn to use the power of Python to train your machine and make predictions and implement the ML algorithm "Random Forest." Use NumPy with Python for array handling, Pandas data frames for Excel files, and matplotlib for data visualization. You will learn to use Random Forest with sklearn, Matplotlib for Python plotting, and SciKit-Learn for Random Forest.
Upon completion, you will Implement the structure of forest, impurity, information gain, partitions, leaf nodes, and decision nodes using Python and create a complete structure for Random Forest using Python to build one tree that lets you create an entire forest. You will write an accuracy calculator function and implement Random Forest on any dataset.
All resources are available at: https://github.com/PacktPublishing/Machine-Learning-Random-Forest-with-Python-from-Scratch-
What you will learn
- Use Random Forest with sklearn and Matplotlib for Python plotting
- Use SciKit-Learn for Random Forest using the titanic dataset
- Learn forest structure, impurity, partition, leaf/decision nodes
- Create a complete Random Forest structure from scratch using Python
- Build one tree that adds up to create a complete forest
- Write accuracy calculator functions and implement them on any dataset
Who this book is for
This course is for you if you want to learn how to program in Python for machine learning or want to make a predictive analysis model.
This course is for someone who is an absolute beginner and has truly little or even zero ideas of machine learning or wants to learn random forest from zero to hero.
