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Top 17 trending interview questions for AI Scientists

Data Science Dojo

Feature engineering: Creating informative features can help improve model performance and reduce overfitting. Technical Skills Implement a simple linear regression model from scratch. Python Explain the steps involved in training a decision tree.

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Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

Data Sourcing. Fundamental to any aspect of data science, it’s difficult to develop accurate predictions or craft a decision tree if you’re garnering insights from inadequate data sources. Objectives and Usage.

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How to build a Machine Learning Model?

Pickl AI

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning?

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Machine Learning for Optimal Performance in AngularJS Development

Mlearning.ai

Using different machine learning algorithms for performance optimization: Several machine learning algorithms can be used for performance optimization, including regression, clustering, and decision trees. Decision tree algorithms can be used to identify performance bottlenecks and suggest optimization strategies.

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Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

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Supervised learning vs Unsupervised learning

Pickl AI

Significantly, Supervised Learning is practical in two types of tasks- Classification: the goal is to predict a categorical label for each input data point Regression: the goal is to predict a continuous value. It includes various algorithms like linear regression, logistic regression, decision trees, bayesian logic, etc.

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Eager Learning and Lazy Learning in Machine Learning: A Comprehensive Comparison

Pickl AI

It constructs a hyperplane to separate different classes during training and uses it to make predictions on new data. Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction.