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)
8 388
students
1.5 hours
content
Mar 2024
last update
$13.99
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
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5635058
udemy ID
30/10/2023
course created date
03/11/2023
course indexed date
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