How to Learn Machine Learning

APRIL 26, 2025

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

Data Science 52

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Pickl AI

APRIL 21, 2025

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.

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Pickl AI

JANUARY 12, 2025

A generative AI company exemplifies this by offering solutions that enable businesses to streamline operations, personalise customer experiences, and optimise workflows through advanced algorithms. Data forms the backbone of AI systems, feeding into the core input for machine learning algorithms to generate their predictions and insights.

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Pickl AI

MAY 29, 2024

Technical Skills Technical skills form the foundation of a Data Scientist’s toolkit, enabling the analysis, manipulation, and interpretation of complex data sets. Machine Learning Algorithms Understanding and implementing Machine Learning Algorithms is a core requirement.

Data Scientist 52

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Pickl AI

MARCH 10, 2023

However, despite being a lucrative career option, Data Scientists face several challenges occasionally. The following blog will discuss the familiar Data Science challenges professionals face daily. Data Pre-processing is a necessary Data Science process because it helps improve the accuracy and reliability of data.

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Pickl AI

JULY 20, 2023

Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more.

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Pickl AI

MAY 15, 2023

Distributed processing is commonly in use for big data analytics, distributed databases and distributed computing frameworks like Hadoop and Spark. Multi-processing: it is the type of data processing in which two or more processors tend to work on the same dataset at the same time. The Data Science courses provided by Pickl.AI

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