Machine Learning DIY
Get latest project developments in Machine Learning from around the world covering topics of ML developments, Programming & Syntax, Code Libraries etc to help encourage the programmer in you.
Developing a Neural Net for Predicting Car Insurance Payout
Developing a neural network predictive model for a new dataset can be challenging.
One approach is to first inspect the dataset and develop ideas for...
How to develop Deep Learning Models for Multi-Output Regression
Multi-output regression involves predicting two or more numerical variables.
Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized...
Using AutoKeras for Classification and Regression
AutoML refers to techniques for automatically discovering the best-performing model for a given dataset.
When applied to neural networks, this involves both discovering the model...
Title Tag Optimization Using Deep Learning Automation
A quick SEO win for most sites is to include the top ranking keyword in the title tags that are missing them.
Think about it...
Common JavaScript Idioms in ReasonML
Explore the differences and similarities in idioms used in JavaScript and ReasonML.
Learning a new programming language is difficult, especially when you switch from a...
Understaing Stochastic Hill Climbing optimization algorithm
Stochastic Hill climbing is an optimization algorithm.
It makes use of randomness as part of the search process. This makes the algorithm appropriate for nonlinear objective...
Developing multinomial logistic regression models in Python
Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems.
Logistic regression, by default, is limited to two-class classification...
Using Stochastic Optimization Algorithms for Feature Selection
Typically, a simpler and better-performing machine learning model can be developed by removing input features (columns) from the training dataset.
This is called feature selection...
Evaluating Machine Learning Algorithms with Train-Test Split
The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not...
Setting Axis for Rows and Columns in NumPy
NumPy arrays provide a fast and efficient way to store and manipulate data in Python.
They are particularly useful for representing data as vectors and...
Python projects for beginners
If you're learning to code, sometimes it can be more fun to work through practical end-to-end projects than to learn the theory.
These 15+ tutorials...
Comparing Machine Learning Algorithms through the Hypothesis Test
Machine learning models are chosen based on their mean performance, often calculated using k-fold cross-validation.
The algorithm with the best mean performance is expected to...