Python SONAR Analytics: Acoustic Exploration Random Forest
Navigate SONAR analytics with Python, gaining practical skills to decode acoustic signals and make informed discoveries
4.58 (12 reviews)
8,387
students
1.5 hours
content
Mar 2024
last update
$19.99
regular price
What you will learn
Introduction to SONAR Analytics: Gain a solid understanding of SONAR data and its relevance in acoustic exploration. Explore fundamentals of acoustic signal
Data Loading and Preprocessing in Python: Learn how to load and preprocess SONAR datasets using Python. Master techniques for cleaning, formatting.
Cross-Validation and Algorithm Evaluation: Understand the importance of cross-validation in model evaluation. Evaluate algorithm performance using metrics
Decision Trees and Random Forest Basics: Explore the foundational concepts of decision trees in machine learning. Understand the basics of the Random Forest
Node Value and Subsampling Techniques: Learn to create terminal node values in decision trees. Explore the concept of subsampling and its role in algorithm
Random Forest Algorithm Implementation: Gain hands-on experience in implementing the Random Forest algorithm in Python.
Testing the Algorithm on SONAR Dataset: Apply the Random Forest algorithm to SONAR datasets for practical insights.
Algorithm Performance Evaluation: Explore methods to assess and evaluate the performance of the Random Forest algorithm.
Real-World Applications and Case Studies: Apply learned concepts to real-world SONAR analytics scenarios.
Practical Skills for Data Science: Develop practical skills in Python programming for data science tasks.
Students will not only possess a deep understanding of SONAR analytics but also have the practical skills to apply Python and the Random Forest algorithm
5635058
udemy ID
10/30/2023
course created date
11/3/2023
course indexed date
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