Data Science in Python: Classification Modeling
Data Science in Python: Classification Modeling
Learn Python for Data Science & Supervised Machine Learning, and build classification models with fun, hands-on projects
Created by Maven Analytics, Chris Bruehl | 10 hours on-demand video course
This is a hands-on, project-based Data Science in Python: Classification Modeling course designed to help you master the foundations for classification modeling in Python. We’ll start by reviewing the 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, 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.
What you’ll learn
- 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
- Build and interpret k-nearest neighbors and logistic regression models using scikit-learn
- 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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Who this course is for:
- Data scientists who want to learn how to build and apply supervised learning models in Python
- Analysts or BI experts looking to learn about classification modeling or transition into a data science role
- Anyone interested in learning one of the most popular open source programming languages in the world
