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Feature Engineering in Machine Learning

Pickl AI

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. What is Feature Engineering? Steps of Feature Engineering 1.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms. Here is a brief description of the same.

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The AI Process

Towards AI

We can apply a data-centric approach by using AutoML or coding a custom test harness to evaluate many algorithms (say 20–30) on the dataset and then choose the top performers (perhaps top 3) for further study, being sure to give preference to simpler algorithms (Occam’s Razor).

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The Easiest Way to Determine Which Scikit-Learn Model Is Perfect for Your Data

Mlearning.ai

This simplifies the process of model selection and evaluation, making it easier than ever to choose the right algorithm for your supervised learning task. You may need to import more libraries for EDA, preprocessing, and so on depending on the dataset you’re dealing with. STEP 1: Install the lazypredict library.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

A Algorithm: A set of rules or instructions for solving a problem or performing a task, often used in data processing and analysis. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

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Meet the winners of the Kelp Wanted challenge

DrivenData Labs

In the Kelp Wanted challenge, participants were called upon to develop algorithms to help map and monitor kelp forests. Winning algorithms will not only advance scientific understanding, but also equip kelp forest managers and policymakers with vital tools to safeguard these vulnerable and vital ecosystems.