Published 1/2024
Created by Maven Analytics,Chris Bruehl
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 171 Lectures ( 9h 53m ) | Size: 3.5 GB
Learn Python for Data Science & Supervised Machine Learning, and build classification models with fun, hands-on projects
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 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.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.COURSE OUTLINE:Intro to Data ScienceIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowClassification 101Review the basics of classification, including key terms, the types and goals of classification modeling, and the modeling workflowPre-Modeling Data Prep & EDARecap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationshipsK-Nearest NeighborsLearn how the k-nearest neighbors (KNN) algorithm classifies data points and practice building KNN models in PythonLogistic RegressionIntroduce logistic regression, learn the math behind the model, and practice fitting them and tuning regularization strengthClassification MetricsLearn how and when to use several important metrics for evaluating classification models, such as precision, recall, F1 score, and ROC-AUCImbalanced DataUnderstand the challenges of modeling imbalanced data and learn strategies for improving model performance in these scenariosDecision TreesBuild and evaluate decision tree models, algorithms that look for the splits in your data that best separate your classesEnsemble ModelsGet familiar with the basics of ensemble models, then dive into specific models like random forests and gradient boosted machines__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:9.5 hours of high-quality video18 homework assignments9 quizzes2 projectsData Science in Python: Classification ebook (250+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you’re an 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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