Mlearning.ai

MAY 23, 2023

The following figure represents the life cycle of data science. It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. What is deep learning?

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Pickl AI

NOVEMBER 2, 2023

Here, we’ll explore why Data Science is indispensable in today’s world. Understanding Data Science At its core, Data Science is all about transforming raw data into actionable information. It includes data collection, data cleaning, data analysis, and interpretation.

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Pickl AI

DECEMBER 10, 2024

Image Data Image features involve identifying visual patterns like edges, shapes, or textures. Methods like Histogram of Oriented Gradients (HOG) or Deep Learning models, particularly Convolutional Neural Networks (CNNs), effectively extract meaningful representations from images. What is Feature Extraction?

Machine Learning 52

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Pickl AI

DECEMBER 16, 2024

Step 3: Data Preprocessing and Exploration Before modeling, it’s essential to preprocess and explore the data thoroughly.This step ensures that you have a clean and well-understood dataset before moving on to modeling. Cleaning Data: Address any missing values or outliers that could skew results.

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Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. This process ensures that the dataset is of high quality and suitable for machine learning. PyTorch: PyTorch is another popular deep learning library that is widely used for training LLMs.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. Haibo Ding is a senior applied scientist at Amazon Machine Learning Solutions Lab. He is broadly interested in Deep Learning and Natural Language Processing.

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Pickl AI

SEPTEMBER 12, 2024

Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Analysis: This step involves applying statistical and Machine Learning techniques to analyse the cleaned data and uncover patterns, trends, and relationships.

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