Day155 - MLOps Review: Introduction to Machine Learning Systems Design (1)
Designing Machine Learning Systems: Business and ML Objectives & Requirements for ML Systems
Recent posts
A New Chapter:: Continuing onward to MLOps
Designing Machine Learning Systems (MLOPs) Review Begins!
Day154 - STAT Review: Unsupervised Learning (7)
Practical Statistics for Data Scientists: Scaling and Categorical Variables (Scaling the Variables, Dominant Variables, Categorical Data, and Gower’s Distanc...
Day153 - STAT Review: Unsupervised Learning (6)
Practical Statistics for Data Scientists: Model-Based Clustering (Multivariate Normal Distribution, Mixtures of Normals & Selecting the Number of Cluster...
Day152 - STAT Review: Unsupervised Learning (5)
Practical Statistics for Data Scientists: Hierarchical Clustering (A Simple Example, the Dendrogram, the Agglomerative Algorithm & Measures of Dissimilar...
Day151 - STAT Review: Unsupervised Learning (4)
Practical Statistics for Data Scientists: K-Means Clustering (2) (Interpreting Clustering Results & Determining the Optimal Number of Clusters K)
Day150 - STAT Review: Unsupervised Learning (3)
Practical Statistics for Data Scientists: K-Means Clustering (1) (A Simple Example & K-Means Algorithm Code Source)
Day149 - STAT Review: Unsupervised Learning (2)
Practical Statistics for Data Scientists: Principal Components Analysis (2) (Formal Definition, Interpreting Components & Correspondence Analysis)
Day148 - STAT Review: Unsupervised Learning (1)
Practical Statistics for Data Scientists: Principal Components Analysis (1) (Unsupervised Learning, A Simple Example and Computing the Principal Components)
Day147 - STAT Review: Statistical Machine Learning (9)
Practical Statistics for Data Scientists: Boosting (2) (Regularization, Hyperparameters & Cross-Validation)