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.

Seberapa baik model Anda?

Menentukan metrik utama

Menilai pengklasifikasi prediksi diabetes

Regresi logistik dan kurva ROC

Membangun model logistic regression

Kurva ROC

ROC AUC

Penyetelan hiperparameter

Penyetelan hyperparameter dengan GridSearchCV

Hyperparameter tuning dengan 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

Kembangkan keterampilan Machine Learning Anda dengan scikit-learn dan pelajari cara menggunakan pustaka Python populer ini untuk melatih model menggunakan data berlabel. Dalam kursus ini, Anda akan belajar membuat prediksi yang kuat, seperti apakah pelanggan akan churn dari bisnis Anda, apakah seseorang mengidap diabetes, hingga mengklasifikasikan genre sebuah lagu. Dengan himpunan data dunia nyata, Anda akan mempelajari cara membangun model prediktif, menyetel parameternya, dan menentukan seberapa baik kinerjanya pada data yang belum pernah dilihat.

Video dilengkapi transkrip langsung yang dapat Anda tampilkan dengan mengklik "Show transcript" di kiri bawah video.									
Glosarium kursus dapat ditemukan di sebelah kanan pada bagian sumber daya.
							
Untuk memperoleh kredit CPE, Anda harus menyelesaikan kursus dan mencapai skor 70% pada asesmen yang memenuhi syarat. Anda dapat menuju ke asesmen dengan mengklik pengumuman kredit CPE di sebelah kanan.

Introduction to Statistics in Python

Tingkatkan keterampilan machine learning Anda dengan scikit-learn di Python. Pelajari prediksi kuat!

Regresi logistik dan kurva ROC

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