Machine Learning: Random Forest with Python from Scratch© [Video]
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-
Free ChapterIntroduction to the Course
-
Introduction to Python
- Hello World
- Introduction to Data Types
- Numbers
- Strings
- Tuples
- Lists
- Sets
- Dictionaries
- Comparison Operators
- Logical Operators, User Input, Game
- Decision Making (if, else, elif)
- Decision Making (nested if)
- Better Coding Practice, Completing the Game
- For Loop
- While Loop
- Simple Functions
- Boolean and Value Returning Function
- Calculator Project
-
Introduction to Machine Learning
-
Random Forest Step-by-Step
- Introduction and Motivation
- How Decision Trees and Random Forest Work
- Pros and Cons of Random Forest
- Introduction to the Final Project
- Using NumPy for Random Forest
- Using Pandas for Random Forest (1)
- Using Pandas for Random Forest (2)
- Reading and Manipulating Dataset
- Using Matplotlib for Data Visualization (1)
- Using Matplotlib for Data Visualization (2)
- Dealing with Missing Values
- Outliers Removal
- Categorical to Numeric Conversion
- Quick Implementation of Random Forest Model
- Feature Importance
- Recursion
- Structure
- Importing Data, Helper Functions
- Question and Partition
- Impurity
- Information Gain
- Best Slip
- Leaf and Decision Node
- How to Build a Tree
- How to Classify
- Accuracy and Error
-
Conclusion
About this
video
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-
- Publication date:
- November 2022
- Publisher
- Packt
- Duration
- 8 hours 20 minutes
- ISBN
- 9781803236803