Review of Python Courses (Part 12)

In Part 11, I summarized my Datacamp courses 31-34. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #35 was Intermediate Data Visualization with Seaborn. This course covers:

• Introduction to Seaborn [histogram vs. sns.distplot()]
• Using the distribution plot
• Regression plots in Seaborn [sns.regplot(), sns.lmplot()]
• Using Seaborn styles [sns.set_style(), sns.despine()]
• Colors in Seaborn
• Customizing with matplotlib (using Axes)
• Categorical plot types
• Regression plots [sns.regplot(), sns.residplot()]
• Matrix plots [sns.heatmap(pd.crosstab())]
• Using FacetGrid, factorplot, lmplot
• Using PairGrid and pairplot
• Using JointGrid and jointplot
• Selecting Seaborn plots

My course #36 was Introduction to Data Visualization with Seaborn (taking #35 before this was an oversight on my part, but everything ended up okay). This course covers:

• Introduction to Seaborn
• Using pandas with Seaborn
• Adding a third variable with hue
• Introduction to relational plots and subplots
• Customizing scatter plots
• Introduction to line plots
• Count plots and bar plots [sns.catplot()]
• Creating a box plot
• Point plots
• Changing plot style and color
• Adding titles and labels (FacetGrid vs. AxesSubplot)

My course #37 was Unsupervised Learning in Python. This course covers:

• Unsupervised learning (from sklearn.cluster import KMeans)
• Evaluating a clustering
• Transforming features for better clustering (from sklearn.preprocessing import StandardScaler)
• Visualizing hierarchies (from scipy.cluster.hierarchy import linkage, dendrogram)
• Cluster labels in hierarchical clustering
• t-SNE for 2-dimensional maps (from sklearn.manifold import TSNE)
• Visualizing the PCA transformation (from sklearn.decomposition import PCA)
• Intrinsic dimension
• Dimension reduction with PCA (from sklearn.decomposition import TruncatedSVD)
• Non-negative matrix factorization (NMF) (from sklearn.decomposition import NMF)
• NMF learns interpretable parts
• Building recommender systems using NMF (From sklearn.preprocessing import normalize)

I will review more classes next time.