18 docs tagged with "machine-learning"

AI strategy is about having a clear view of how to enable the organization to utilize AI capabilities. An AI strategy should encompass the following subsets of strategies.

- The churn rate measures a company's loss of subscribers for a given period of time.

As you can see from the above image, confident learning is about estimating the likelyhood of the data being labeled correctly based upon the confidence of the model. If the model confidence is above the threshold confidence (The Tj parameter, tdog, tfox tcow) and if the confidence of the model prediction is higher than the threshold but the label is different, then we predict a wrong label

What is data science? It is a bunch of different jobs bunched together and given the tie of AI to make a company sound innovative.

This book covers the fundamentals of designing machine learning systems. It goes through the entire lifecycle of a machine learning system and then discusses the ecosystem and the challenges and cases that need to be considered.

This book is about how to make features for machine learning models and implement them into models. The book goes into natural language text, tabular data, and image data. It contains discussions about how to implement good engineering practices in feature engineering.

This book is an introduction to unsupervised machine learning techniques and practices. It introduces methods of unsupervised learning for clustering, correlations and time series analysis. It analyses models and provides guidance on how to use them.

This book is about machine learning design patterns and discussions around those—concepts in machine learning for this. And therefore, it is a good reminder of the concepts and core tenants of machine learning.

This book is about the practical implementation of machine learning models. It goes through why machine learning should be used, how to implement it, and how to execute in all phasers of the machine learning life-cycle.

Decision Trees

The ML Design Sprint is a modification of the design sprint workshop, where the goal is to give the project relevant context on the problem, the data, and the resources available. The goal of the ML Design Sprint is to decide on the goal of the model, the input features of the model, and how the model should be evaluated. The design sprint brings together Subject Matter Experts, Users, Product Owners Data Scientists, and ML Engineers together to quickly understand the problem, the potential solutions, and the risks. ML Design Sprint should shorten the duration of the scoping and exploratory analysis phases by bringing the analysts and experts together.

This book is mostly about different ways of thinking about models, in the context of making models of real-world phenomena. It dives into the cons and benefits of each mindset and tries to explain how knowing each is a good advantage.

Natural Language Processing (NLP) is using machine learning techniques to work with text.

This book is a no-nonsense book about practical MLOps and how you should approach it to solve business problems. The book takes an even more hardline approach to automation and focuses on the concept of Kaizen ML, where continuous improvement and striving to make the feedback loop even shorter and the process more and more seamless.

This book is an easy and good introduction to recommendation systems. It is meant to introduce the concept of recommendation systems and, from there, give concepts and methods on how to make them. This book is about usage rather than the theoretical background.

Snowflake is a company providing a data warehouse.

This is a history book of people who revolutionized finance with math, game theory, and computers to model how the market work. However, the real world is not a model, and quants are also humans, as the financial crisis showed.

Random Cut Forrest