Rekomendasikan artis musik bagian I

Dalam latihan ini dan berikutnya, Anda akan menggunakan apa yang telah Anda pelajari tentang NMF untuk merekomendasikan artis musik populer! Anda diberikan array jarang artists yang baris-barisnya merepresentasikan artis dan kolom-kolomnya merepresentasikan pengguna. Setiap entri menunjukkan berapa kali setiap artis didengarkan oleh setiap pengguna.

Pada latihan ini, bangun sebuah pipeline dan transformasikan array menjadi fitur NMF yang dinormalisasi. Langkah pertama dalam pipeline, MaxAbsScaler, mentransformasi data sehingga semua pengguna memiliki pengaruh yang sama terhadap model, terlepas dari berapa banyak artis berbeda yang telah mereka dengarkan. Pada latihan berikutnya, Anda akan menggunakan fitur NMF yang telah dinormalisasi ini untuk melakukan rekomendasi!

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

Petunjuk latihan

Impor:
- NMF dari sklearn.decomposition.
- Normalizer dan MaxAbsScaler dari sklearn.preprocessing.
- make_pipeline dari sklearn.pipeline.
Buat instance MaxAbsScaler bernama scaler.
Buat instance NMF dengan 20 komponen bernama nmf.
Buat instance Normalizer bernama normalizer.
Buat pipeline bernama pipeline yang merangkai scaler, nmf, dan normalizer.
Terapkan metode .fit_transform() dari pipeline ke artists. Simpan hasilnya ke norm_features.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Perform the necessary imports
from ____ import ____
from ____ import ____, ____
from ____ import ____

# Create a MaxAbsScaler: scaler
scaler = ____

# Create an NMF model: nmf
nmf = ____

# Create a Normalizer: normalizer
normalizer = ____

# Create a pipeline: pipeline
pipeline = ____

# Apply fit_transform to artists: norm_features
norm_features = ____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizing hierarchies Exercise 2: How many merges?Exercise 3: Hierarchical clustering of the grain data Exercise 4: Hierarchies of stocks Exercise 5: Cluster labels in hierarchical clustering Exercise 6: Which clusters are closest?Exercise 7: Different linkage, different hierarchical clustering!Exercise 8: Intermediate clusterings Exercise 9: Extracting the cluster labels Exercise 10: t-SNE for 2-dimensional maps Exercise 11: t-SNE visualization of grain dataset Exercise 12: A t-SNE map of the stock market

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Data non-negatif Exercise 3: Penerapan NMF pada artikel Wikipedia Exercise 4: Fitur NMF dari artikel Wikipedia Exercise 5: NMF merekonstruksi sampel Exercise 6: NMF mempelajari komponen yang dapat ditafsirkan Exercise 7: NMF mempelajari topik dokumen Exercise 8: Jelajahi himpunan data digit LED Exercise 9: NMF mempelajari bagian-bagian dari citra Exercise 10: PCA tidak mempelajari bagian-bagian Exercise 11: Membangun sistem rekomendasi menggunakan NMF Exercise 12: Artikel mana yang mirip dengan 'Cristiano Ronaldo'?Exercise 13: Rekomendasikan artis musik bagian I

Latihan Saat Ini

Exercise 14: Rekomendasikan artis musik bagian II Exercise 15: Penutup