Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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Supervised Learning Machine Learning Machine Learning Deep Learning 264

Mlearning.ai

NOVEMBER 23, 2023

This Only Applies to Supervised Learning Introduction If you’re like me then you probably like a more intuitive way of doing things. When it comes to machine learning, we often have that one (or two or three) “go-to” model(s) that we tend to rely on for most problems. STEP 1: Install the lazypredict library.

Supervised Learning 52

Supervised Learning Cross Validation EDA Machine Learning 52

Pickl AI

NOVEMBER 4, 2024

Here are some recommended projects to help reinforce your learning: Data Analysis Project Start with a dataset from sources like Kaggle or UCI Machine Learning Repository. Perform exploratory Data Analysis (EDA) using Pandas and visualise your findings with Matplotlib or Seaborn.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

JULY 12, 2024

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. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Becoming Human

MAY 15, 2023

For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis. First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date.

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Pickl AI

FEBRUARY 12, 2024

Differentiate between supervised and unsupervised learning algorithms. Supervised learning algorithms learn from labelled data, where each input is associated with a corresponding output label. Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Kaggle

JULY 29, 2020

What supervised learning methods did you use? The early days of the effort were spent on EDA and exchanging ideas with other members of the community. The text is then broken down into sentences per document, and those sentences are mapped to sentence embeddings using a BM25 + fastText method described in this Medium article.

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ETL Data Scientist Data Science Machine Learning 71