Data Without Labels — Models and Algorithms for Practical Unsupervised
#MachineLearning:
amzn.to/4q5bbYz
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