Remove Data Preparation Remove Decision Trees Remove Hadoop
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Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

Understanding the MLOps Lifecycle The MLOps lifecycle consists of several critical stages, each with its unique challenges: Data Ingestion: Collecting data from various sources and ensuring it’s available for analysis. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Data Transformation Transforming data prepares it for Machine Learning models. It’s simple but effective for many problems like predicting house prices.

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Introduction to applied data science 101: Key concepts and methodologies 

Data Science Dojo

It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed. From decision trees and neural networks to regression models and clustering algorithms, a variety of techniques come under the umbrella of machine learning.

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Predicting the Future of Data Science

Pickl AI

Augmented Analytics Augmented analytics is revolutionising the way businesses analyse data by integrating Artificial Intelligence (AI) and Machine Learning (ML) into analytics processes. Dive Deep into Machine Learning and AI Technologies Study core Machine Learning concepts, including algorithms like linear regression and decision trees.