Data Science In Python: Classification Modeling

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)

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 About This Series

Lecture 3 Course Structure & Outline

Lecture 4 Course Structure & Outline

Lecture 4 READ ME: Important Notes for New Students

Lecture 5 DOWNLOAD: Course Resources

Lecture 6 Introducing the Course Project

Lecture 7 Setting Expectations

Lecture 8 Jupyter Installation & Launch

Section 2: Intro to Data Science

Lecture 9 What is Data Science?

Lecture 10 The Data Science Skillset

Lecture 11 What is Machine Learning?

Lecture 12 Common Machine Learning Algorithms

Lecture 13 Data Science Workflow

Lecture 14 Data Prep & EDA Steps

Lecture 15 Modeling Steps

Lecture 16 Classification Modeling

Lecture 17 Key Takeaways

Section 3: Classification 101

Lecture 18 Classification 101

Lecture 19 Goals of Classification

Lecture 20 Types of Classification

Lecture 21 Classification Modeling Workflow

Lecture 22 Key Takeaways

Section 4: Data Prep & EDA

Lecture 23 EDA For Classification

Lecture 24 Defining a Target

Lecture 25 DEMO: Defining a Target

Lecture 26 Exploring the Target

Lecture 27 Exploring the Features

Lecture 28 DEMO: Exploring the Features

Lecture 29 ASSIGNMENT: Exploring the Target & Features

Lecture 30 SOLUTION: Exploring the Target & Features

Lecture 31 Correlation

Lecture 32 PRO TIP: Correlation Matrix

Lecture 33 DEMO: Correlation Matrix

Lecture 34 Feature-Target Relationships

Lecture 35 Feature-Feature Relationships

Lecture 36 PRO TIP: Pair Plots

Lecture 37 ASSIGNMENT: Exploring Relationships

Lecture 38 SOLUTION: Exploring Relationships

Lecture 39 Feature Engineering Overview

Lecture 40 Numeric Feature Engineering

Lecture 41 Dummy Variables

Lecture 42 Binning Categories

Lecture 43 DEMO: Feature Engineering

Lecture 44 Data Splitting

Lecture 45 Preparing Data for Modeling

Lecture 46 ASSIGNMENT: Preparing the Data for Modeling

Lecture 47 SOLUTION: Prepare the Data for Modeling

Lecture 48 Key Takeaways

Section 5: K-Nearest Neighbors

Lecture 49 K-Nearest Neighbors

Lecture 50 The KNN Workflow

Lecture 51 KNN in Python

Lecture 52 Model Accuracy

Lecture 53 Confusion Matrix

Lecture 54 DEMO: Confusion Matrix

Lecture 55 ASSIGNMENT: Fitting a Simple KNN Model

Lecture 56 SOLUTION: Fitting a Simple KNN Model

Lecture 57 Hyperparameter Tuning

Lecture 58 Overfitting & Validation

Lecture 59 DEMO: Hyperparameter Tuning

Lecture 60 Hard vs. Soft Classification

Lecture 61 DEMO: Probability vs. Event Rate

Lecture 62 ASSIGNMENT: Tuning a KNN Model

Lecture 63 SOLUTION: Tuning a KNN Model

Lecture 64 Pros & Cons of KNN

Lecture 65 Key Takeaways

Section 6: Logistic Regression

Lecture 66 Logistic Regression

Lecture 67 Logistic vs. Linear Regression

Lecture 68 The Logistic Function

Lecture 69 Likelihood

Lecture 70 Multiple Logistic Regression

Lecture 71 The Logistic Regression Workflow

Lecture 72 Logistic Regression in Python

Lecture 73 Interpreting Coefficients

Lecture 74 ASSIGNMENT: Logistic Regression

Lecture 75 SOLUTION: Logistic Regression

Lecture 76 Feature Engineering & Selection

Lecture 77 Regularization

Lecture 78 Tuning a Regularized Model

Lecture 79 DEMO: Regularized Logistic Regression

Lecture 80 ASSIGNMENT: Regularized Logistic Regression

Lecture 81 SOLUTION: Regularized Logistic Regression

Lecture 82 Multi-class Logistic Regression

Lecture 83 ASSIGNMENT: Multi-class Logistic Regression

Lecture 84 SOLUTION: Multi-class Logistic Regression

Lecture 85 Pros & Cons of Logistic Regression

Lecture 86 Key Takeaways

Section 7: Classification Metrics

Lecture 87 Classification Metrics

Lecture 88 Accuracy, Precision & Recall

Lecture 89 DEMO: Accuracy, Precision & Recall

Lecture 90 PRO TIP: F1 Score

Lecture 91 ASSIGNMENT: Model Metrics

Lecture 92 SOLUTION: Model Metrics

Lecture 93 Soft Classification

Lecture 94 DEMO: Leveraging Soft Classification

Lecture 95 PRO TIP: Precision-Recall & F1 Curves

Lecture 96 DEMO: Plotting Precision-Recall & F1 Curves

Lecture 97 The ROC Curve & AUC

Lecture 98 DEMO: The ROC Curve & AUC

Lecture 99 Classification Metrics Recap

Lecture 100 ASSIGNMENT: Threshold Shifting

Lecture 101 SOLUTION: Threshold Shifting

Lecture 102 Multi-class Metrics

Lecture 103 Multi-class Metrics in Python

Lecture 104 ASSIGNMENT: Multi-class Metrics

Lecture 105 SOLUTION: Multi-class Metrics

Lecture 106 Key Takeaways

Section 8: Imbalanced Data

Lecture 107 Imbalanced Data

Lecture 108 Managing Imbalanced Data

Lecture 109 Threshold Shifting

Lecture 110 Sampling Strategies

Lecture 111 Oversampling

Lecture 112 Oversampling in Python

Lecture 113 DEMO: Oversampling

Lecture 114 SMOTE

Lecture 115 SMOTE in Python

Lecture 116 Undersampling

Lecture 117 Undersampling in Python

Lecture 118 ASSIGNMENT: Sampling Methods

Lecture 119 SOLUTION: Sampling Methods

Lecture 120 Changing Class Weights

Lecture 121 DEMO: Changing Class Weights

Lecture 122 ASSIGNMENT: Changing Class Weights

Lecture 123 SOLUTION: Changing Class Weights

Lecture 124 Imbalanced Data Recap

Lecture 125 Key Takeaways

Section 9: Mid-Course Project

Lecture 126 Project Brief

Lecture 127 Solution Walkthrough

Section 10: Decision Trees

Lecture 128 Decision Trees

Lecture 129 Entropy

Lecture 130 Decision Tree Predictions

Lecture 131 Decision Trees in Python

Lecture 132 DEMO: Decision Trees

Lecture 133 Feature Importance

Lecture 134 ASSIGNMENT: Decision Trees

Lecture 135 SOLUTION: Decision Trees

Lecture 136 Hyperparameter Tuning for Decision Trees

Lecture 137 DEMO: Hyperparameter Tuning

Lecture 138 ASSIGNMENT: Tuned Decision Tree

Lecture 139 SOLUTION: Tuned Decision Tree

Lecture 140 Pros & Cons of Decision Trees

Lecture 141 Key Takeaways

Section 11: Ensemble Models

Lecture 142 Ensemble Models

Lecture 143 Simple Ensemble Models

Lecture 144 DEMO: Simple Ensemble Models

Lecture 145 ASSIGNMENT: Simple Ensemble Models

Lecture 146 SOLUTION: Simple Ensemble Models

Lecture 147 Random Forests

Lecture 148 Fitting Random Forests in Python

Lecture 149 Hyperparameter Tuning for Random Forests

Lecture 150 PRO TIP: Random Search

Lecture 151 Pros & Cons of Random Forests

Lecture 152 ASSIGNMENT: Random Forests

Lecture 153 SOLUTION: Random Forests

Lecture 154 Gradient Boosting

Lecture 155 Gradient Boosting in Python

Lecture 156 Hyperparameter Tuning for Gradient Boosting

Lecture 157 DEMO: Hyperparameter Tuning for Gradient Boosting

Lecture 158 Pros & Cons of Gradient Boosting

Lecture 159 ASSIGNMENT: Gradient Boosting

Lecture 160 SOLUTION: Gradient Boosting

Lecture 161 PRO TIP: SHAP Values

Lecture 162 DEMO: SHAP Values

Lecture 163 Key Takeaways

Section 12: Classification Summary

Lecture 164 Recap: Classification Models & Workflow

Lecture 165 Pros & Cons of Classification Models

Lecture 166 DEMO: Production Pipeline & Deployment

Lecture 167 Looking Ahead: Unsupervised Learning

Section 13: Final Project

Lecture 168 Project Brief

Lecture 169 Solution Walkthrough

Section 14: Next Steps

Lecture 170 BONUS LESSON

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