Kirk Borne

Data Without Labels — Models and Algorithms for Practical Unsupervised Machine Learning: https://t.co/8adzWdcbrU 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🔶Fundamental building blocks and concepts of machine learning and unsupervised learning 🔶Data cleaning for structured and unstructured data like text and images 🔶Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering 🔶Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE 🔶Association rule algorithms like aPriori, ECLAT, SPADE 🔶Unsupervised time series clustering, Gaussian Mixture models, and statistical methods 🔶Building neural networks such as GANs and autoencoders 🔶Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling 🔶Association rule algorithms like aPriori, ECLAT, and SPADE 🔶Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask 🔶How to interpret the results of unsupervised learning 🔶Choosing the right algorithm for your problem 🔶Deploying unsupervised learning to production 🔶Maintenance and refresh of an ML solution