Dataconomy

MAY 25, 2023

Here are some ways AI enhances IoT devices: Advanced data analysis AI algorithms can process and analyze vast volumes of IoT-generated data. By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data.

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IBM Journey to AI blog

JULY 6, 2023

Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem. What is machine learning? It requires data science tools to first clean, prepare and analyze unstructured big data.

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Machine Learning Machine Learning Data Science Big Data 60

Pickl AI

SEPTEMBER 3, 2023

49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales. On the other hand, 48% use ML and AI for gaining insights into the prospects and customers.

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IBM Journey to AI blog

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. For instance, a parsing model could identify the subject, verb and object of a complete sentence.

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. What is MLOps?

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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ODSC - Open Data Science

JANUARY 5, 2024

Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools. Pandas provides a fast and efficient way to work with tabular data. It is widely used in data science, finance, and other fields where data analysis is essential.

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Python K-nearest Neighbors Data Science Data Visualization 52

Pickl AI

MAY 10, 2023

Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification. Certainly, these predictions and classification help in uncovering valuable insights in data mining projects. Hyperplanes are useful in separating the data points into groups.

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Dataconomy

SEPTEMBER 1, 2023

So how can the technology of our time, machine learning, be used to improve the quality and length of human life? Heart disease stands as one of the foremost global causes of mortality today, presenting a critical challenge in clinical data analysis. Dealing with missing values is a common challenge in medical data analysis.

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Mlearning.ai

MAY 23, 2023

The following Venn diagram depicts the difference between data science and data analytics clearly: 3. Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. It is introduced into an ML Model when an ML algorithm is made highly complex.

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Heartbeat

MARCH 8, 2023

Text categorization is supported by a number of programming languages, including R, Python, and Weka, but the main focus of this article will be text classification with R. R Language Source: i2tutorial R, a popular open-source programming language, is used for statistical computation and data analysis.

Machine Learning 52

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Mlearning.ai

FEBRUARY 15, 2023

The accuracy of the ML model indicates how many times it was correct overall. While the amount of data available was limited, we have tried to solve the problem of generalization by using methods such as stopwords removal, tokenization, lemmatization, dropout and early stopping. Cambridge: MIT Press. Senekane, M., & Taele, B.

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