Loke many other species, humans have evolved to be dominant at times and subservient at other times (because groups that rally behind leaders often have better chances of survival). We can design and build AI systems that are subservient to humans at all times, with zero desire
AI
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Essential Learning Resources for Data Science and Machine Learning
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End of this thread! If you are looking to learn more about
Data Science
ML/DL/AI
Analytics
Math & Statistics
Resources
LLMs
MLOps Then, Don't forget to follow me at @avikumart_ for upcoming posts -
AGI Token: Agents, Nodes, and Future Value Generation
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$AGI By: AGI.eth The #AGIToken : 1). The #AGI Agents could generate most of the light cone of all future value. 2). The operation of an #AGINode NFT could yield $AGI (utility token / active income exclusively). 3). $AGI for products / services generated by the #AGIAgents.
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Real-time AI Models in Business Decision-Making Processes
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It often involves applying live models within an organization’s decision-making processes-for example, real-time personalization web page, product recommender system, or scoring of marketing leads.
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Model Deployment: From Creation to Practical Implementation
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6. Deployment The creation of the model is generally not the end of the project. Even if the purpose of the model is to analyze the data and increase the understanding of the data, the knowledge or insight gained from the modeling needs to…
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Presenting AI Models to End Users Across Business Levels
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…be presented so that the end users of the model can use it. End users could be operational-level staff, business executives, or customers as well.
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Data Mining Results Decision Phase Completion
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At the end of this phase, a decision on using the data mining results should be reached.
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Evaluation Phase in Data Science Project Lifecycle
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5. Evaluation The evaluation phase is one of the most crucial phases of any data science project lifecycle. At this stage in your project, you must have built machine-learning models.
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Evaluating AI Models on Unseen Data and Performance Metrics
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Models might be performing well on your training data, but it is necessary to test and evaluate it on unseen data for the model to achieve its objectives. Appropriate evaluation metrics are measured and well-tested at this stage.
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Data Preparation Phase Requirements for AI Techniques
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Some techniques have specific requirements for the format of data it needs. Therefore, going back to the data preparation phase is often necessary at this stage.