Muziekartiesten aanbevelen, deel II

Stel dat je een grote fan bent van Bruce Springsteen — welke andere artiesten vind je dan waarschijnlijk ook leuk? Gebruik je NMF-features uit de vorige oefening en de cosinusovereenkomst om vergelijkbare artiesten te vinden. Een oplossing voor de vorige oefening is al uitgevoerd, dus norm_features is een array met de genormaliseerde NMF-features als rijen. De namen van de artiesten zijn beschikbaar als de lijst artist_names.

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Oefeninstructies

Importeer pandas als pd.
Maak een DataFrame df van norm_features, met artist_names als index.
Gebruik de .loc[] accessor van df om de rij van 'Bruce Springsteen' te selecteren. Ken het resultaat toe aan artist.
Pas de .dot()-methode van df toe op artist om het inwendige product van elke rij met artist te berekenen. Sla het resultaat op als similarities.
Print het resultaat van de .nlargest()-methode van similarities om de artiesten te tonen die het meest lijken op 'Bruce Springsteen'.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import pandas
____

# Create a DataFrame: df
df = ____

# Select row of 'Bruce Springsteen': artist
artist = df.loc[____]

# Compute cosine similarities: similarities
similarities = ____

# Display those with highest cosine similarity
____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus 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: Niet-negatieve matrixfactorisatie (NMF)Exercise 2: Niet-negatieve data Exercise 3: NMF toegepast op Wikipedia-artikelen Exercise 4: NMF-features van de Wikipedia-artikelen Exercise 5: NMF reconstrueert steekproeven Exercise 6: NMF leert interpreteerbare onderdelen Exercise 7: NMF leert onderwerpen van documenten Exercise 8: Verken de LED-cijferdataset Exercise 9: NMF leert de delen van afbeeldingen Exercise 10: PCA leert geen delen Exercise 11: Aanbevelingssystemen bouwen met NMF Exercise 12: Welke artikelen lijken op 'Cristiano Ronaldo'?Exercise 13: Muziekaartiesten aanbevelen deel I Exercise 14: Muziekartiesten aanbevelen, deel II

Huidige oefening

Exercise 15: Tot slot