Core, key and reposted information on AI based technologies #AI #AIKingdom777
The following are four data deep dive aspects for laying the foundation for AI based technologies:
Data Literacy - use of generative AI technologies are leveraged efficiently and effectively.
Data Readiness - core and key data is ready to be able to be returned in prompts.
Data Quality - data that is to be utilized and returned in prompt technologies is useful and correct.
Data Trust - data that is utilized is indeed from a trusted source.
What is Artificial Intelligence (AI)?: A Simplified Overview
https://www.amazon.com/dp/B0DT7TKKCC/
Artificial Intelligence (AI) is a rapidly evolving field that involves creating systems capable of performing tasks which typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and interaction.
As an Amazon Associate, I earn from qualifying purchases.
Therefore, this overview covers the most common aspects of AI. The information can be utilized for personal, educational, or corporate usage. It is envisioned that one utilizes the material for discussion and demonstration purposes. After each key topic, a series of generative prompts for example purposes is depicted.
Contents
Overview of AI:
Overview of Generative AI:
Using Generative AI:
Generative AI Prompt Examples:
Overview of Algorithm:
Algorithm Prompt Examples:
Overview of Autonomous System:
Autonomous Systems Prompt Examples:
Overview of Machine Learning:
Machine Learning Prompt Examples:
Overview of Supervised Learning:
Supervised Learning Prompt Examples:
Overview of Unsupervised Learning:
Unsupervised Learning Example Prompts:
Overview of Reinforcement Learning:
Reinforcement Learning Example Prompts:
Overview of Deep Learning:
Deep Learning Example Prompts:
Overview of Fuzzy Logic:
Fuzzy Logic Example Prompts:
Overview of AI Engineer Roles:
About the Author:
Notes:
The following are the top three predictive models in AI:
Linear Regression: One of the simplest and most commonly utilized predictive models. It's used to predict a continuous outcome variable based on one or more predictor variables. It works well for problems where the relationship between the variables is linear.
Decision Trees: Versatile and utilized for both classification and regression tasks. They work by splitting the data into subsets based on feature values, creating a tree-like structure of decisions. Decision trees are easy to interpret and can capture non-linear relationships.
Neural Networks: More complex models that are effective for tasks involving large amounts of data and complex patterns. Neural networks consist of layers of interconnected nodes (neurons) and can model highly intricate relationships in the data.
Brother P-Touch, PTM95, Handy Label Maker, 9 Type Styles, 8 Deco Mode Patterns, Navy Blue, Blue Gray
As an Amazon Associate, I earn from qualifying purchases.
The following are three cost-effective AI predictive analysis generative prompts:
The following are three artificial intelligence formulas regarding precision and recall:
Precision
Precision is the percentage of true positives regarding the returns of the artificial intelligence (AI).
Precision = True Positive / All Predictive Positives * 100
Recall
Recall is the fraction of positive cases which are correctly identified via artificial intelligence (AI).
Recall = True Positive / True Positive + False Negative
F1 Score
F1 score is the measure of balance between precision and recall of the artificial intelligence (AI).
The following are the top 4 AI clustering prompts:
The following are the top three predictive analytics formulas:
1) Linear Regression: Utilized to predict a continuous value. It models the relationship between two variables by fitting a linear equation to observed data.
Formula:
Example: Predicting sales based on advertising spend.
2) Logistic Regression: Utilized for classification tasks to predict the probability of a binary outcome (0 or 1).
Formula:
Example: Predicting whether a customer will churn or not.
3) k-Nearest Neighbors (k-NN): A simple, instance-based learning algorithm utilized for both classification and regression.
Formula: No explicit formula; it classifies based on the majority vote of the k-nearest neighbors.
Example: Classifying a new data point based on the labels of its nearest neighbors.
The following are the top five everyday useful prompts:
Amazon Basics Rubber Hex Dumbbell Hand Weight
As an Amazon Associate I earn from qualifying purchases.
The following are the top 3 prompts in predictive analytics:
Amazon Basics Rubber Hex Dumbbell Hand Weight
As an Amazon Associate I earn from qualifying purchases.
The following are the top three essential AI terms:
Artificial General Intelligence (AGI):
Machine Learning (ML):
Neural Networks:
The following are the top three deep learning aspects:
Neural Networks:
Training Techniques:
Applications:
The following are the top three key techniques used in predictive analytics:
