Python Data Science: Classification Modeling [11/2024, ENG] torrent

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Python Data Science: Classification Modeling

Год выпуска: 11/2024
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/data-science-in-python-classification/
Автор: Chris Bruehl, Maven Analytics
Продолжительность: 9h 48m 45s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Английский
Описание:

Learn Python for data science & supervised machine learning, and build classification models w/ a top Python instructor!

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 Requirements We strongly recommend taking our Data Prep & EDA and Regression courses before this one
Jupyter Notebooks (free download, we'll walk through the install)
Familiarity with base Python and Pandas is recommended, but not required 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.
[spoiler="COURSE OUTLINE:"]
Intro to Data Science in Python Introduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflow Classification 101 Review the basics of classification, including key terms, the types and goals of classification modeling, and the modeling workflow Pre-Modeling Data Prep & EDA Recap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationships K-Nearest Neighbors Learn how the k-nearest neighbors (KNN) algorithm classifies data points and practice building KNN models in Python Logistic Regression Introduce logistic regression, learn the math behind the model, and practice fitting them and tuning regularization strength Classification Metrics Learn how and when to use several important metrics for evaluating classification models, such as precision, recall, F1 score, and ROC-AUC Imbalanced Data Understand the challenges of modeling imbalanced data and learn strategies for improving model performance in these scenarios Decision Trees Build and evaluate decision tree models, algorithms that look for the splits in your data that best separate your classes Ensemble Models Get familiar with the basics of ensemble models, then dive into specific models like random forests and gradient boosted machines
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[spoiler="Ready to dive in? Join today and get immediate, LIFETIME access to the following:"]
9.5 hours of high-quality video
18 homework assignments
9 quizzes
2 projects
Python Data Science: Classification ebook (250+ pages)
Downloadable project files & solutions
Expert support and Q&A forum
30-day Udemy satisfaction guarantee
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If you're a business intelligence professional or aspiring data scientist looking for an introduction to the world of classification modeling with Python, this is the course for you.

Happy learning!

-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)

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Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!
[spoiler="See why our courses are among the TOP-RATED on Udemy:"]
See why our courses are among the TOP-RATED on Udemy:

"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C.

"This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M.

"Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.
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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 Формат видео: MP4
Видео: avc, 1280x720, 16:9, 30.000 к/с, 214 кб/с
Аудио: aac lc sbr, 44.1 кгц, 63.1 кб/с, 2 аудио
[spoiler="Изменения/Changes"]
Version 2024/11 compared to the 2024/1 has decreased by 2 lessons and 1 minutes in duration. English subtitles were also added to the course.
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[spoiler="Скриншоты"]

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