Rocket-Powered Data Science

JULY 6, 2021

The almost forgotten “orphan” in these architectures, Fog Computing (living between edge and cloud), is now moving to a more significant status in data and analytics architecture design. 7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all. And the goodness doesn’t stop there.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

datapine

MAY 14, 2019

This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. click for book source**.

Data Science 350

Data Science Machine Learning Big Data Data-driven 350

Rocket-Powered Data Science

DECEMBER 16, 2018

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Rocket-Powered Data Science

DECEMBER 15, 2018

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. In those intermediate steps it serves as an evaluation (or validation) metric. This challenge is known as the cold-start problem !

Machine Learning 171

Machine Learning Optimization Metrics Deep Learning 171

Smart Data Collective

FEBRUARY 14, 2021

Do Your Research with Data Mining. Big data makes it a lot easier to research new opportunities. there are a lot of great big data repositories on customer desires and marketing trends. You need to use Hadoop tools to mine this data and find out more about your target customers and product requirements.

Data-driven 106

Data-driven Big Data Deep Learning Data mining 106

Sisense

APRIL 10, 2020

Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Libraries used for NLP are: NLTK, gensim, SpaCy , glove, and Scikit-Learn. Every library has its own purpose and benefits.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54