Gesamtlänge aller Episoden: 1 day 15 hours 40 minutes
Comparison of different data storage options when working with your ML models.
Dimensions, size, and shape of Numpy ndarrays / TensorFlow tensors, and methods for transforming those.
Run your code + visualizations in the browser: iPython / Jupyter Notebooks.
EDA + charting. DataFrame info/describe, imputing strategies. Useful charts like histograms and correlation matrices.
matplotlib, Seaborn, Bokeh, D3, Tableau, Power BI, QlikView, Excel
NLTK: swiss army knife. Gensim: LDA topic modeling, n-grams. spaCy: linguistics. transformers: high-level business NLP tasks.
Kmeans (sklearn vs FAISS), finding n_clusters via inertia/silhouette, Agglomorative, DBSCAN/HDBSCAN