Remove AWS Remove Clustering Remove Hypothesis Testing
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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Unsupervised Learning Unsupervised learning involves training models on data without labels, where the system tries to find hidden patterns or structures.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. It is highly configurable and can integrate with other tools like Git, Docker, and AWS. Things to learn: AWS , GCP , or Microsoft Azure anyone of them.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

These models may include regression, classification, clustering, and more. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Cloud Platforms: AWS, Azure, Google Cloud, etc. Machine Learning: Supervised and unsupervised learning techniques, deep learning, etc.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. Have you worked with cloud-based data platforms like AWS, Google Cloud, or Azure? You’re tasked with predicting sales for a retail store. What approach would you take?