Random Forest in Machine Learning with Python Online Course

Random Forest in Machine Learning with Python Online Course

This course introduces machine learning and its real-world applications, guiding you through key Python concepts like variables, loops, classes, and data handling with NumPy and Pandas. You’ll learn to train models, make predictions, and implement the Random Forest algorithm using SciKit-Learn, exploring concepts such as decision nodes, leaf nodes, information gain, and forest structure. With practical exercises and visualization using Matplotlib, you’ll gain hands-on experience building and evaluating machine learning models from scratch.

Who should take this course?

This course is ideal for data scientists, machine learning enthusiasts, and Python developers who want to learn how to implement and optimize Random Forest models. It’s also suitable for students and professionals aiming to enhance predictive analytics skills using ensemble learning techniques.

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

Course Outline

Introduction to the Course

• Introduction and Instructor
• Motivation for the Course
• Past, Present, and Future of Machine Learning
• Course Overview

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

• Let's Introduce Machine Learning
• Kids versus Computer Learning
• Dataset
• Labels and Features
• Outliers
• Model and Training
• Overfitting and Underfitting
• Accuracy and Error
• Formats of Data
• Types of Learning
• Classification versus Regression
• Clustering
• Recap, Flow of Machine Learning Project

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

• Concluding remarks