Data Without Labels

In Data Without Labels you’ll 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