Random Forest in Machine Learning with Python Online Course

Random Forest in Machine Learning with Python Online Course

The Random Forest in Machine Learning with Python Online Course will guide you through the foundational concepts of machine learning, the Random Forest algorithm, and its implementation in Python. Perfect for beginners and aspiring data scientists, this course equips you with practical knowledge and hands-on experience to excel in the field of machine learning.

Key Benefits

• Harness Python’s Power: Use Python to develop machine learning models that can make accurate predictions.
• Master Data Preprocessing: Learn how to prepare datasets for machine learning algorithms effectively.
• Implement Random Forest: Gain a thorough understanding of the Random Forest algorithm and how to apply it to real-world problems.
• Learn Industry Tools: Work with libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn for data analysis and visualization.
• Build Models from Scratch: Develop a complete Random Forest structure using Python, starting from a single tree to an entire forest.
• Test and Validate: Write accuracy calculator functions and implement Random Forest on various datasets for performance evaluation.

What you will Learn?

Python and Machine Learning Foundations

• Learn Python basics: Variables, loops, classes, and more.
• Introduction to machine learning concepts and applications.

Random Forest Algorithm

• Understand forest structure, impurity, leaf nodes, and decision nodes.
• Learn to build and implement Random Forest from scratch.

Data Handling and Visualization

• Use NumPy for arrays and Pandas for data frames.
• Visualize data with Matplotlib and analyze models with SciKit-Learn.

Hands-On Implementation

• Build and test Random Forest models using Python.
• Apply Random Forest to datasets for practical problem-solving, including survival prediction using the Titanic dataset.

Accuracy Evaluation

• Write custom functions to calculate model accuracy.
• Validate Random Forest implementations on various datasets.

Who should take this Course For?

This course is designed for anyone who wants to:

• Learn how to program in Python with a focus on machine learning.
• Build predictive models using the Random Forest algorithm.
• Understand machine learning concepts from scratch, even with no prior knowledge.

Knowledge Gained

By completing the Random Forest in Machine Learning with Python Exam, you will acquire the following knowledge:

• Machine Learning Concepts: A thorough understanding of machine learning principles, its applications, and the motivation behind its use in real-world scenarios.
• Python Programming: Solid foundational knowledge of Python programming, including variables, loops, decision-making statements, and object-oriented programming concepts.
• Random Forest Algorithm: Comprehensive insight into the Random Forest algorithm, including its structure, impurity measures, decision nodes, and partitions.
• Data Preprocessing: Techniques to clean, preprocess, and prepare data for machine learning algorithms.
• Data Visualization: Skills to visually represent datasets using Matplotlib and understand patterns effectively.
• Using Python Libraries: Hands-on experience with popular libraries like NumPy for numerical operations, Pandas for data manipulation, and SciKit-Learn for implementing machine learning models.
• Model Building and Testing: The ability to build, test, and deploy Random Forest models from scratch, including accuracy evaluation and validation on different datasets.

Course Outline

The Random Forest in Machine Learning with Python Online Course - 

Domain 1. Introduction to the Course (FREE CHAPTER)

• Meet the Instructor and Introduction
• Purpose and Motivation Behind the Course
• The Evolution of Machine Learning: Past, Present, and Future
• Course Overview and Learning Objectives

Domain 2. Introduction to Python

Writing Your First Program: "Hello World"

• Understanding Data Types - Numbers, Strings, Tuples, Lists, Sets, Dictionaries
• Operators in Python - Comparison Operators and Logical Operators
• User Input and Building a Simple Game
• Decision Making in Python - If, Else, and Elif Statements, Nested If Statements, Writing Better Code Practices for Decision Making and Completing the Game Logic
• Loops in Python - For Loop, While Loop,
• Functions in Python - Simple Functions  and Boolean and Value-Returning Functions
• Python Project: Building a Calculator

Domain 3. Introduction to Machine Learning

• What is Machine Learning?
• Comparing Human Learning with Machine Learning
• Understanding Datasets - Labels and Features and Handling Outliers
• Building and Training Models - Overfitting vs. Underfitting and Accuracy and Error Metrics
• Data Formats for Machine Learning
• Types of Learning - Supervised and Unsupervised Learning, Classification vs. Regression and Clustering Algorithms
• Recap: Machine Learning Project Workflow

Domain 4. Random Forest Step-by-Step

• Introduction and Motivation for Using Random Forest
• How Decision Trees and Random Forest Algorithms Work
• Advantages and Limitations of Random Forest
• Overview of the Final Project
• Tools and Libraries for Random Forest Implementation
  • Using NumPy for Random Forest Operations
  • Using Pandas for Dataset Manipulation (Part 1 and Part 2)
  • Visualizing Data with Matplotlib (Part 1 and Part 2)
• Data Preprocessing Steps
  • Handling Missing Values
  • Removing Outliers
  • Converting Categorical Data to Numeric
• Quick Implementation of a Random Forest Model
• Feature Importance in Random Forest
• Recursion and Understanding Tree Structures
  • Importing Data and Writing Helper Functions
  • Defining Questions and Partitions
  • Calculating Impurity and Information Gain
  • Identifying the Best Split for the Dataset
  • Creating Leaf and Decision Nodes
  • Building a Decision Tree Step-by-Step
• Classifying Data with Random Forest
• Evaluating the Model: Accuracy and Error Metrics