IBM Journey to AI blog

MARCH 21, 2024

Accelerated data processing Efficient data processing pipelines are critical for AI workflows, especially those involving large datasets. Leveraging distributed storage and processing frameworks such as Apache Hadoop, Spark or Dask accelerates data ingestion, transformation and analysis.

Apache Hadoop 68

Apache Hadoop AI AI Natural Language Processing 68

Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

Data Science 52

Data Science Data Scientist Apache Hadoop Machine Learning 52

Pickl AI

JULY 20, 2023

5. Text Analytics and Natural Language Processing (NLP) Projects: These projects involve analyzing unstructured text data, such as customer reviews, social media posts, emails, and news articles. To ascertain the general sentiment and deal with any potential problems, use natural language processing (NLP) tools.

Analytics 40

Analytics Analytics Big Data Big Data 40

Pickl AI

JULY 18, 2023

Additionally, its natural language processing capabilities and Machine Learning frameworks like TensorFlow and scikit-learn make Python an all-in-one language for Data Science. Its speed and performance make it a favored language for big data analytics, where efficiency and scalability are paramount.

Data Science 52

Data Science SQL Data Scientist Apache Hadoop 52

Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. After that, move towards unsupervised learning methods like clustering and dimensionality reduction.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

JULY 10, 2023

The programming language can handle Big Data and perform effective data analysis and statistical modelling. Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling. R is a popular programming language and environment widely used in the field of data science.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52