Machine Learning Planning and architectures

There are multiple types of projects on machine learning, so the phases and steps are different. I will try to reduce to some basic type of projects. Basic project plans (main phases) Machine learning solution based on a Product Technology assessment = 2 – 3 days. Production trial = 8 – 12 days. Application deployment … Read more

Tuning a Machine Learning Model

I continue taking some basic notes of the book “Enterprise Artificial Intelligence and Machine Learning for Managers“. Tuning a machine learning model is an iterative process. Data scientists typically run numerous experiments to train and evaluate models, trying out different features, different loss functions, different AI/ML models, and adjusting model parameters and hyper-parameters. Feature engineering … Read more

Support vector machine (SVM)

The basis Support vector machine (SVM) is a supervised learning method. It can be used for regression or classification purposes. SVM is useful for hyper-text categorization, classification of images, recognition of characters… The basic visual idea is the creation of planes (lines) to separate features. The position of these planes can be adjusted to maximized the … Read more

Machine learning, source of errors

Before to start What is an error? Observation prediction error = Target – Prediction = Bias + Variance + Noise The main sources of errors are Bias and Variability (variance). Underfitting or overfitting. Underclustering or overclustering. Improper validation (after the training). It could be that comes from the wrong validation set. It is important to divide … Read more

k-means clustering

The basis K-means clustering is an unsupervised learning method. The aim is to find clusters and the CentroIDs that can potentially identify the What is a cluster? a set of data points grouped in the same category. What is a CentroID? center or average of a given cluster. What is “k”? the number of CentroIDs … Read more

Naive Bayes classification

The basis It’s based on Bayes’ theorem (check the wikipedia link, and see how complex the decision trees could be). Assumes predictors contribute independently to the classification. Works well in supervised learning problems. Works with continuous and discrete data. Can work with discrete data sets. It is not sensitive to non-correlating “predictors”. Naives Bayes plot Example: … Read more