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

Program

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

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.

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

Ingegnere AI associato per scienziati dei dati

Scienziato dei dati associato  in Python

Ingegnere dell'apprendimento automatico

Fondamenti di apprendimento automatico in Python

Scienziato dell'apprendimento automatico in Python

Apprendimento automatico supervisionato in Python

Core Curriculum Manager, DataCamp

Classifying labels of unseen data

k-Nearest Neighbors

KNN Intuition

Using scikit-learn to fit a classifier

Predicting on unlabeled data

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_28314/subtitles/course_28314_a47d13b49a2a722157414c88367799e9.vtt

Classification - The classification challenge

The classification challenge

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