# Search results for "Bayesian Modeling"

## Resources for Getting Started With Probability in Machine Learning

Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and linear algebra, probability is another foundational field that supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty. Many aspects of machine learning are uncertain, including, most critically, observations from the problem […]

## A Tour of Machine Learning Algorithms

In this post, we will take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to […]

## Neural Networks: Tricks of the Trade Review

Deep learning neural networks are challenging to configure and train. There are decades of tips and tricks spread across hundreds of research papers, source code, and in the heads of academics and practitioners. The book “Neural Networks: Tricks of the Trade” originally published in 1998 and updated in 2012 at the cusp of the deep […]

## Comparing Classical and Machine Learning Algorithms for Time Series Forecasting

Machine learning and deep learning methods are often reported to be the key solution to all predictive modeling problems. An important recent study evaluated and compared the performance of many classical and modern machine learning and deep learning methods on a large and diverse set of more than 1,000 univariate time series forecasting problems. The […]

## How and When to Use a Calibrated Classification Model with scikit-learn

Instead of predicting class values directly for a classification problem, it can be convenient to predict the probability of an observation belonging to each possible class. Predicting probabilities allows some flexibility including deciding how to interpret the probabilities, presenting predictions with uncertainty, and providing more nuanced ways to evaluate the skill of the model. Predicted […]

## How to Configure the Number of Layers and Nodes in a Neural Network

Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. You must specify values for these parameters when configuring your network. The most reliable way to configure these hyperparameters for your specific predictive modeling problem is […]

## How Much Training Data is Required for Machine Learning?

The amount of data you need depends both on the complexity of your problem and on the complexity of your chosen algorithm. This is a fact, but does not help you if you are at the pointy end of a machine learning project. A common question I get asked is: How much data do I […]

## EBooks

Frustrated with one-off articles and too much math? Take the Next Step and Get Tutorial-Based Playbooks that will Guide You to a Specific Result Welcome to: the Machine Learning Mastery EBook Catalog Beginner | Intermediate | Advanced | Bundles | Donate Would you like to support Machine Learning Mastery? Consider a one time donation or […]

## Master Machine Learning Algorithms

Master Machine Learning Algorithms Finally Pull Back The Curtain And See How They Work With Clear Descriptions, Step-By-Step Tutorials and Working Examples in Spreadsheets You Learn Best By Implementing Algorithms From Scratch…But You Need Help With The First Step: The Math Developers Learn Fast By Trying Things Out… I’m a developer and I feel like I don’t […]

## Data Science From Scratch: Book Review

Programmers learn by implementing techniques from scratch. It is a type of learning that is perhaps slower than other types of learning, but fuller in that all of the micro decisions involved become intimate. The implementation is owned from head to tail. In this post we take a close look at Joel Grus popular book […]