In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Machine learning with scikit-learn

Binary classification

The supervised learning workflow

The classification challenge

k-Nearest Neighbors: Fit

k-Nearest Neighbors: Predict

Measuring model performance

Train/test split + computing accuracy

Overfitting and underfitting

Visualizing model complexity

Classification

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting. 

Introduction to regression

Creating features

Building a linear regression model

Visualizing a linear regression model

The basics of linear regression

Fit and predict for regression

Regression performance

Cross-validation

Cross-validation for R-squared

Analyzing cross-validation metrics

Regularized regression

Regularized regression: Ridge

Lasso regression for feature importance

Regression

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

How good is your model?

Deciding on a primary metric

Assessing a diabetes prediction classifier

Logistic regression and the ROC curve

Building a logistic regression model

The ROC curve

ROC AUC

Hyperparameter tuning

Hyperparameter tuning with GridSearchCV

Hyperparameter tuning with RandomizedSearchCV

Fine-Tuning Your Model

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Preprocessing data

Creating dummy variables

Regression with categorical features

Handling missing data

Dropping missing data

Pipeline for song genre prediction: I

Pipeline for song genre prediction: II

Centering and scaling

Centering and scaling for regression

Centering and scaling for classification

Evaluating multiple models

Visualizing regression model performance

Predicting on the test set

Visualizing classification model performance

Pipeline for predicting song popularity

Congratulations

Preprocessing and Pipelines

Advertising and Sales

Diabetes

Telecom Churn

Music

Course Glossary

Grow your machine learning skills with scikit-learn and discover how to use this popular Python library to train models using labeled data. In this course, you'll learn how to make powerful predictions, such as whether a customer is will churn from your business, whether an individual has diabetes, and even how to tell classify the genre of a song. Using real-world datasets, you'll find out how to build predictive models, tune their parameters, and determine how well they will perform with unseen data.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.									
The course glossary can be found on the right in the resources section.
							
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Introduction to Statistics in Python

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Machine Learning with scikit-learn

Supervised Learning with scikit-learn

Associate AI Engineer for Data Scientists

Associate Data Scientist  in Python

Machine Learning Engineer

Machine Learning Fundamentals in Python

Wetenschapper op het gebied van machine learning in Python

Supervised Machine Learning in Python

Core Curriculum Manager, DataCamp

Different models for different problems

It's all in the metrics

A note on scaling

Evaluating classification models

Visualizing results

Test set performance

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_28314/subtitles/course_28314_188cea781aeb7e12dfc11ceba3e5d15b.vtt

Preprocessing and Pipelines - Evaluating multiple models

Evaluating multiple models

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