Analytics Vidhya

JANUARY 8, 2023

Hey, are you the data science geek who spends hours coding, learning a new language, or just exploring new avenues of data science? If all of these describe you, then this Blogathon announcement is for you! Analytics Vidhya is back with its 28th Edition of blogathon, a place where you can share your knowledge about […].

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Data Science Analytics Analytics Hadoop 329

AWS Machine Learning Blog

MAY 16, 2024

Therefore, we decided to introduce a deep learning-based recommendation algorithm that can identify not only linear relationships in the data, but also more complex relationships. With Amazon EMR, which provides fully managed environments like Apache Hadoop and Spark, we were able to process data faster.

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Analytics Vidhya

NOVEMBER 7, 2022

Hello, fellow data science enthusiasts, did you miss imparting your knowledge in the previous blogathon due to a time crunch? Well, it’s okay because we are back with another blogathon where you can share your wisdom on numerous data science topics and connect with the community of fellow enthusiasts.

Data Science 307

Data Science Analytics Analytics Hadoop 307

Smart Data Collective

MAY 8, 2022

Above all, there needs to be a set methodology for data mining, collection, and structure within the organization before data is run through a deep learning algorithm or machine learning. With the evolution of technology and the introduction of Hadoop, Big Data analytics have become more accessible.

Big Data Analytics 122

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Mlearning.ai

JANUARY 28, 2023

Scikit-Learn: Scikit-Learn is a machine learning library that makes it easy to train and deploy machine learning models. It has a wide range of features, including data preprocessing, feature extraction, deep learning training, and model evaluation. How Do I Use These Libraries?

Machine Learning 52

Machine Learning Machine Learning Data Science ML 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

AWS Machine Learning Blog

DECEMBER 18, 2023

The service uses deep learning techniques to handle complex data patterns and enables businesses to generate accurate forecasts even with minimal historical data. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

Clustering 121

Clustering AWS Database ML 121

ODSC - Open Data Science

OCTOBER 7, 2024

Techniques such as parallel data processing and distributed data storage systems, like Hadoop or cloud-native solutions, allow data scientists to ingest and store large volumes of data effectively. Building Scalable Data Pipelines The foundation of any AI pipeline is the data it consumes.

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Pickl AI

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Machine Learning: Supervised and unsupervised learning techniques, deep learning, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

AWS Machine Learning Blog

FEBRUARY 13, 2024

With a strong background in computer vision, data science, and deep learning, he holds a postgraduate degree from IIT Bombay. Nanda has over 18 years of experience working in Java/J2EE, Spring technologies, and big data frameworks using Hadoop and Apache Spark.

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IBM Journey to AI blog

JULY 6, 2023

Today, machine learning has evolved to the point that engineers need to know applied mathematics, computer programming, statistical methods, probability concepts, data structure and other computer science fundamentals, and big data tools such as Hadoop and Hive. Python is the most common programming language used in machine learning.

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Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

MAY 29, 2024

The curriculum includes subjects like linear algebra, calculus, probability, and statistics, essential for understanding Machine Learning and Deep Learning Models. Machine Learning and Deep Learning In Data Science, Machine Learning is super important.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. Neural Networks These models simulate the structure of the human brain, allowing them to learn complex patterns in large datasets. Neural networks are the foundation of Deep Learning techniques.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

JULY 31, 2023

It automates the process of hyperparameter tuning, making it easy to find the best hyperparameters for a given machine learning problem. This is especially useful for deep learning models, which can have many hyperparameters that need to be optimized. Using Comet saves time and reduces the risk of human error.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

ODSC - Open Data Science

JANUARY 7, 2025

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. Deep Learning Deep learning is a cornerstone of modern AI, and its applications are expanding rapidly.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

DagsHub

OCTOBER 23, 2024

Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

APRIL 18, 2023

Here is what you need to add to your resume Analysed Built Conducted Created Collaborated Developed Integrated Led Managed Partnered Support Designed Showcase Your Technical Skills In addition to using the right words and phrases in your resume, you should also highlight the key skills.

Data Science 52

Data Science Machine Learning Machine Learning Data Scientist 52

DagsHub

NOVEMBER 11, 2024

These vector databases store complex data by transforming the original unstructured data into numerical embeddings; this is enabled through deep learning models. AI also plays an important role in this process because it uses deep learning methods to create embeddings that find all the key features of the original data.

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Pickl AI

JUNE 19, 2024

For instance, courses focusing on big data might require knowledge of Hadoop or Spark, while those emphasizing machine learning might delve into deep learning frameworks like TensorFlow or PyTorch.

Data Science 52

Data Science Data Scientist Hypothesis Testing Natural Language Processing 52

Mlearning.ai

AUGUST 18, 2023

However, data scientists in healthcare have employed deep learning technologies to enable easier analysis. For example, deep learning algorithms have already shown impressive results in detecting 26 skin conditions on par with certified dermatologists.

Data Science 52

Data Science Data Scientist Internet of Things Apache Hadoop 52

Pickl AI

JULY 20, 2023

Image Recognition with Deep Learning: Use Python with TensorFlow or PyTorch to build an image recognition model (e.g., Big data technology, data pretreatment, statistical analysis, and machine learning methodologies must be thoroughly understood for these applications. CNN) and classify images from a large dataset (e.g.,

Analytics 40

Analytics Analytics Big Data Big Data 40

DagsHub

AUGUST 23, 2024

Source: [link] Torch by Acceldata (not connected to Torch/PyTorch deep learning frameworks) is a data observability platform that combines data quality, pipeline monitoring, and system performance management. With this tool, you can implement and monitor data quality rules across different data sources.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Data Science Dojo

MAY 24, 2024

For example, predictive maintenance in manufacturing uses machine learning to anticipate equipment failures before they occur, reducing downtime and saving costs. Deep Learning Deep learning is a subset of machine learning based on artificial neural networks, where the model learns to perform tasks directly from text, images, or sounds.

Machine Learning 195

Machine Learning Deep Learning Deep Learning Machine Learning 195

Data Science Dojo

AUGUST 30, 2023

From decision trees and neural networks to regression models and clustering algorithms, a variety of techniques come under the umbrella of machine learning. Technologies like Hadoop and Spark enable the processing and analysis of massive datasets in a distributed and parallel manner.

Data Science 195

Data Science Hypothesis Testing Machine Learning Machine Learning 195

Pickl AI

APRIL 2, 2024

Machine Learning: Subset of AI that enables systems to learn from data without being explicitly programmed. Supervised Learning: Learning from labeled data to make predictions or decisions. Unsupervised Learning: Finding patterns or insights from unlabeled data.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52