Python Data Science: Classification Modeling
Udemy Coupon for Python Data Science: Classification Modeling. Find Out Other Data Science Courses and Tutorials from Udemy Learning with Discount Coupon Codes. Learn Python for data science & supervised machine learning, and build classification models w/ a top Python instructor!
Course Description
This is a hands-on, project-based course designed to help you master the foundations for classification modeling and supervised machine learning in Python. We’ll start by reviewing the Python data science workflow, discussing the primary goals & types of classification algorithms, and do a deep dive into the classification modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis (EDA), leverage feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into train, test, and validation datasets.
From there, we’ll fit K-Nearest Neighbors & Logistic Regression models, and build an intuition for interpreting their coefficients and evaluating their performance using tools like confusion matrices and metrics like accuracy, precision, and recall. We’ll also cover techniques for modeling imbalanced data, including threshold tuning, sampling methods like oversampling & SMOTE, and adjusting class weights in the model cost function.
Throughout the course, you’ll play the role of Data Scientist for the risk management department at Maven National Bank. Using the skills you learn throughout the course, you’ll use Python to explore their data and build classification models to accurately determine which customers have high, medium, and low credit risk based on their profiles.
Last but not least, you’ll learn to build and evaluate decision tree models for classification. You’ll fit, visualize, and fine-tune these models using Python, then apply your knowledge to more advanced ensemble models like random forests and gradient boosted machines.
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- Master the foundations of supervised Machine Learning & classification modeling in Python
- Perform exploratory data analysis on model features and targets
- Apply feature engineering techniques and split the data into training, test and validation sets
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- Evaluate model performance using tools like confusion matrices and metrics like accuracy, precision, recall, and F1
- Learn techniques for modeling imbalanced data, including threshold tuning, sampling methods, and adjusting class weights
- Build, tune, and evaluate decision tree models for classification, including advanced ensemble models like random forests and gradient boosted machines
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