Pickl AI
AUGUST 1, 2023
Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks.
ODSC - Open Data Science
AUGUST 15, 2023
You’ll get hands-on practice with unsupervised learning techniques, such as K-Means clustering, and classification algorithms like decision trees and random forest. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.
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Towards AI
FEBRUARY 20, 2024
Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., Speech recognition: Enables voice assistants like Siri and Alexa to understand our spoken words.
Dataconomy
MARCH 13, 2023
With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. What is required to build an AI system?
Pickl AI
MAY 10, 2023
ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm. Consequently, each brand of the decision tree will yield a distinct result.
Pickl AI
NOVEMBER 8, 2023
Decision Tree) Making Predictions Evaluating Model Accuracy (Classification) Feature Scaling (Standardization) Getting Started Before diving into the intricacies of Scikit-Learn, let’s start with the basics. The cheat sheet helps you select the right one for your specific task, be it regression, classification, or clustering.
Dataconomy
JUNE 13, 2023
These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.
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