Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 1, 2024

Data Preparation for Demand Forecasting High-quality data is the cornerstone of effective demand forecasting. Just like building a house requires a strong foundation, building a reliable forecast requires clean and well-organized data. They are particularly effective when dealing with high-dimensional data.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

PyImageSearch

SEPTEMBER 16, 2024

Supervised Learning These methods require labeled data to train the model. The model learns to distinguish between normal and abnormal data points. For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data.

Clustering 56

Clustering Algorithm Machine Learning Machine Learning 56

phData

AUGUST 24, 2023

Various machine learning algorithms can be used for credit scoring and decisioning, including logistic regression, decision trees, random forests, support vector machines, and neural networks. Data Preparation The first step in the process is data collection and preparation.

ML 52

ML ML Machine Learning Machine Learning 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. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52