Descriptions
Data Science in Python: Classification, This course teaches foundational Python skills for applying classification models to real-world data. You’ll learn how to use K-Nearest Neighbors, Logistic Regression, Decision Trees, Random Forests, and Gradient Boosted Machines, building practical experience with each technique. The curriculum covers the theory behind each model, step-by-step implementation in Python, and guidance for evaluating model performance and interpreting results. By the end, you’ll be able to choose and apply the right classification approach for your data science projects, confidently analyze outcomes, and communicate findings to stakeholders.
What you’ll learn
- Understand the theory and intuition behind common classification models
- Implement K-Nearest Neighbors, Logistic Regression, Decision Trees, Random Forests, and Gradient Boosted Machines in Python
- Evaluate model performance and interpret classification results
- Choose the right classification technique for different data science scenarios
Who this course is for
- Aspiring data scientists seeking hands-on experience with Python classification models
- Analysts and professionals wanting to apply machine learning to real-world datasets
- Anyone looking to build practical skills in supervised learning and model evaluation
Specificatoin of Data Science in Python: Classification
- Publisher : Maven Analytics
- Teacher : Chris Bruehl
- Language : English
- Level : All Levels
- Duration : 16 hours
Content of Data Science in Python: Classification
Requirements
- We strongly recommend taking our Data Prep & EDA and Regression courses first
- Jupyter Notebooks (free download, we’ll walk through the install)
- Familiarity with base Python and Pandas is recommended, but not required
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : English
Quality: 1080p
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
1.42 GB
