Description
Data Science in Python: Classification Modeling Course. Data Science in Python: Categorical Modeling Course. This is a hands-on, project-based course designed to help you learn the fundamentals of categorical modeling in Python. We’ll start with an overview of data science workflows, discuss the primary goals and types of classification algorithms, and take a detailed look at the classification modeling steps we’ll use throughout the course. You’ll learn to perform exploratory data analysis, use feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into training, test, and validation datasets. From there, we’ll fit K-Nearest Neighbors and logistic regression models and develop a feel 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 thresholding, sampling methods such as oversampling and SMOTE, and adjusting class weights in the model’s cost function. During the course, you’ll take on the role of a data scientist for Maven National Bank’s risk management department. 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 are high, medium, and low credit risk based on their profiles. Lastly, you’ll learn how to build and evaluate decision tree models for classification. You’ll fit, visualize, and refine these models using Python, then apply your knowledge to more advanced ensemble models such as random forests and gradient boosting machines. Course Summary:
- An introduction to data science
- Provide an introduction to data science and machine learning, review fundamental skills, and introduce each step of the data science workflow.
- Classification 101
- Understand the fundamentals of classification, including key terms, types and purposes of classification modeling, and modeling workflows.
- Data preparation and EDA before modelling
- Review the data preparation and EDA steps required for modeling, including key techniques for target discovery, attributes, and relationships.
- K-nearest neighbors
- Learn how the k-nearest neighbor (KNN) algorithm classifies data points and practice building KNN models in Python.
- logistic regression
- Introduce logistic regression, learn the mathematics behind the model, and practice fitting it and tuning regularization performance.
- Classification criteria
- Learn how and when to use several important metrics to evaluate classification models, such as precision, recall, F1 score, and ROC AUC.
- Unbalanced data
- Understand the challenges of modeling imbalanced data and learn strategies to improve model performance in these scenarios.
- Decision trees
- Build and evaluate decision tree models, algorithms that search your data for partitions that best separate your classes.
- Group models
- Learn the basics of ensemble models and then move on to specific models such as random forests and gradient boosting machines.
If you are an aspiring data scientist looking for an introduction to the world of classification modeling with Python, this course is for you.
What you will learn in the Data Science in Python: Classification Modeling course
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Master the fundamentals of supervised machine learning and classification modeling in Python
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Conduct exploratory data analysis on model features and objectives
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Apply feature engineering techniques and split the data into training, test and validation sets
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Creating and interpreting k-nearest neighbors and logistic regression models with scikit-learn
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Evaluate model performance using tools such as confusion matrices and metrics such as accuracy, precision, recall, and F1
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Learn techniques for modeling unbalanced data, including thresholds, sampling methods, and adjusting class weights.
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Build, optimize, and evaluate decision tree models for classification, including advanced ensemble models such as random forests and gradient boosted machines.
This course is suitable for people who
- Data scientists who want to learn how to build and deploy supervised learning models in Python
- BI analysts or experts who want to learn about classification modeling or take on a role in data science
- Anyone interested in learning one of the world’s most popular open source programming languages
Course Specifications for Data Science in Python: Classification Modeling
- Editor: Udemy
- Teacher: Maven Analytics , Chris Brühl
- Training level: beginner to advanced
- Training duration: 9 hours and 53 minutes
- Number of courses: 171
Course topics on 1/2024
Prerequisites for the course “Data Science in Python: Classification Modeling”.
- We strongly recommend taking our Data Prep & EDA and Regression courses before this course
- Jupyter Notebooks (free download, we will install it for you)
- Familiarity with basic Python and Pandas is recommended but not required
Course pictures
Sample video of the course
installation Guide
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Download link
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Size
2.6GB