Towards AI

JULY 19, 2023

(Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. Data Preparation Photo by Bonnie Kittle […]

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Pickl AI

JULY 12, 2024

Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes. This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. They are handy for high-dimensional data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. Ethical considerations are crucial in developing fair Machine Learning solutions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Pickl AI

OCTOBER 15, 2024

Start by collecting data relevant to your problem, ensuring it’s diverse and representative. After collecting the data, focus on data cleaning, which includes handling missing values, correcting errors, and ensuring consistency. Data preparation also involves feature engineering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

Machine Learning 59

Machine Learning Machine Learning ML ML 59