Remove Data Collection Remove Deep Learning Remove Modeling
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How to Build a Real Estate Price Prediction Model?

Analytics Vidhya

Introduction As a data scientist, you have the power to revolutionize the real estate industry by developing models that can accurately predict house prices. This blog post will teach you how to build a real estate price prediction model from start to finish. appeared first on Analytics Vidhya.

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AI adoption in the enterprise 2020

O'Reilly on Data

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

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Bringing an AI Product to Market

O'Reilly on Data

You must detect when the model has become stale, and retrain it as necessary. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment. Modeling and Evaluation.

Marketing 363
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The road to Software 2.0

O'Reilly on Data

We can collect many examples of what we want the program to do and what not to do (examples of correct and incorrect behavior), label them appropriately, and train a model to perform correctly on new inputs. In short, we can use machine learning to automate software development itself. Instead, we can program by example.

Software 263
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The quest for high-quality data

O'Reilly on Data

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

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What you need to know about product management for AI

O'Reilly on Data

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.