Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.
https://ocdevel.com/mlg
Gesamtlänge aller Episoden: 1 day 15 hours 40 minutes
episode 43: MLG 032 Cartesian Similarity Metrics
L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product
episode 42: MLA 011 Practical Clustering
Kmeans (sklearn vs FAISS), finding n_clusters via inertia/silhouette, Agglomorative, DBSCAN/HDBSCAN
episode 41: MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK
NLTK: swiss army knife. Gensim: LDA topic modeling, n-grams. spaCy: linguistics. transformers: high-level business NLP tasks.
episode 31: 031 The Podcasts Return
The podcasts return with new content, especially about NLP: BERT, transformers, spaCy, Gensim, NLTK. Accompanied by a community project - Gnothi, a journal that uses AI to provide insights and resources. Website project . Share the website on social...
episode 39: MLA 009 Charting tools
matplotlib, Seaborn, Bokeh, D3, Tableau, Power BI, QlikView, Excel
episode 38: MLA 008 Exploratory Data Analysis
EDA + charting. DataFrame info/describe, imputing strategies. Useful charts like histograms and correlation matrices.
episode 37: MLA 007 Jupyter Notebooks
Run your code + visualizations in the browser: iPython / Jupyter Notebooks.
episode 35: MLA 005 Shapes & Sizes
Dimensions, size, and shape of Numpy ndarrays / TensorFlow tensors, and methods for transforming those.