What you'll get
  • 1+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Understand SONAR data and its applications in acoustic exploration
  • Load, clean, and preprocess SONAR datasets using Python
  • Apply cross-validation and evaluate algorithm performance
  • Learn the fundamentals of Decision Trees and Random Forest
  • Implement Random Forest models in Python
  • Optimize model performance and validate predictions
  • Work with real-world SONAR datasets and case studies
  • Develop practical Python data science skills

Content

Courses No. of Hours Certificates Details
Random Forest Algorithm in Machine Learning1h 27m✔View Curriculum

Description

This course introduces learners to data science and machine learning with Python, focusing on analyzing the SONAR dataset. This course is for beginners and experienced users alike, combining theory and practice to identify patterns and build models.

What You Will Learn

  • Data Loading & Preprocessing: Learn how to efficiently load, explore, and prepare datasets for analysis using Python.

  • Cross-Validation & Metrics: Understand dataset splitting, cross-validation, and key algorithm performance metrics.

  • Decision Trees & Node Values: Explore fundamental concepts like node values and subsampling to build robust decision trees.

  • Random Forest Algorithm: Implement the Random Forest algorithm on the SONAR dataset to gain practical experience with ensemble learning techniques.

  • Model Evaluation: Learn to assess algorithm performance and refine models for accurate predictions.

Course Highlights

  • Step-by-step guidance from data exploration to advanced modeling

  • Hands-on exercises using real-world SONAR data

  • Practical insights into building and evaluating machine learning models

  • Applicable for learners at all skill levels, from beginners to experienced Python users.

By the end of this course, participants will have the skills to analyze datasets, build machine learning models, and evaluate their performance using Python, making it a perfect foundation for a career in data science.

Requirements

  • Basic understanding of Python programming

  • Familiarity with fundamental machine learning concepts

  • Understanding of mathematical concepts such as statistics and probability

  • No prior experience with the SONAR dataset required.

Target Audience

  • Data science enthusiasts and Python programmers

  • Students, researchers, and aspiring data scientists

  • Data analysts, ML practitioners, and AI engineers

  • Oceanographers, marine scientists, and acoustic technology professionals

  • Engineers and professionals in SONAR or underwater analytics industries.