Building Classification Models with scikit-learn
This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.
What you'll learn
Perhaps the most ground-breaking advances in machine learning have come from applying machine learning to classification problems.
In this course, Building Classification Models with scikit-learn you will gain the ability to enumerate the different types of classification algorithms and correctly implement them in scikit-learn.
First, you will learn what classification seeks to achieve, and how to evaluate classifiers using accuracy, precision, recall, and ROC curves.
Next, you will discover how to implement various classification techniques such as logistic regression, and Naive Bayes classification.
You will then understand other more advanced forms of classification, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent.
Finally, you will round out the course by understanding the hyperparameters that these various classification models possess, and how these can be optimized.
When you’re finished with this course, you will have the skills and knowledge to select the correct classification algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 1m
- Classification as a Machine Learning Problem 4m
- Logistic Regression Intuition 6m
- Cross Entropy Intuition 2m
- Accuracy, Precision, and Recall 6m
- Determining Decision Threshold Using ROC Curves 7m
- Types of Classification 4m
- Module Summary 1m
- Module Overview 1m
- Installing and Setting up scikit-learn 3m
- Exploring the Titanic Dataset 7m
- Visualizing Relationships in the Data 5m
- Preprocessing the Data 4m
- Training a Logistic Regression Binary Classifier 5m
- Calculating Accuracy, Precision and Recall for the Classification Model 5m
- Defining Helper Functions to Train and Evaluate Classification Models 7m
- Module Summary 1m
- Module Overview 1m
- Choosing Classification Algorithms 2m
- Linear Discriminant Analysis and Quadratic Discriminant Analysis 7m
- Implementing Linear Discriminant Analysis Classification 4m
- Implementing Quadratic Discriminant Analysis Classification 2m
- Stochastic Gradient Descent 3m
- Implementing Stochastic Gradient Descent Classification 3m
- Support Vector Machines 7m
- Implementing Support Vector Classification 3m
- Nearest Neighbors 3m
- Implementing K-nearest-neighbors Classification 1m
- Decision Trees 3m
- Implementing Decision Tree Classification 3m
- Naive Bayes 4m
- Implementing Naive Bayes Classification 2m
- Module Summary 2m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more