IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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Dataconomy

FEBRUARY 24, 2023

This data should be relevant, accurate, and comprehensive. Choose the appropriate algorithm: Select the AI algorithm that best suits the problem you want to solve. Several algorithms are available, including decision trees, neural networks, and support vector machines.

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Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms.

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Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

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Heartbeat

OCTOBER 25, 2023

Next-generation sequencing (NGS) platforms have dramatically increased the speed and reduced the cost of DNA sequencing, leading to the generation of vast amounts of genomic data. Developing benchmark datasets and standardized evaluation metrics is necessary to assess algorithm performance and facilitate comparisons between other methods.

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Pickl AI

JULY 14, 2023

Evolution of AI The evolution of Artificial Intelligence (AI) spans several decades and has witnessed significant advancements in theory, algorithms, and applications. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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IBM Journey to AI blog

AUGUST 28, 2023

As it pertains to social media data, text mining algorithms (and by extension, text analysis) allow businesses to extract, analyze and interpret linguistic data from comments, posts, customer reviews and other text on social media platforms and leverage those data sources to improve products, services and processes.

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning?

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Pickl AI

APRIL 9, 2025

A simple example could be an early chess-playing program that evaluated moves based on predefined rules and search algorithms. This led to the rise of Machine Learning (ML). Machine Learning is a subset of Artificial Intelligence. The Core Idea of Machine Learning: Give the machine data, and let it figure out the rules itself.

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ODSC - Open Data Science

JANUARY 7, 2025

Just as a writer needs to know core skills like sentence structure and grammar, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and soon. Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus.

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Heartbeat

MARCH 21, 2023

I was interested to see what types of problems were solved and which particular algorithms were used with the different loss functions. I decided that aggregating this data would give me a rough idea about what loss functions were commonly being used to solve the different problems. These are two separate lists).

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Pickl AI

SEPTEMBER 12, 2024

Basic Data Science Terms Familiarity with key concepts also fosters confidence when presenting findings to stakeholders. Below is an alphabetical list of essential Data Science terms that every Data Analyst should know. Anomaly Detection: Identifying unusual patterns or outliers in data that do not conform to expected behaviour.

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Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

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Pickl AI

SEPTEMBER 23, 2024

We will also guide you through the best AI and Data Science courses to help you gain the skills needed in this rapidly growing field. Understanding Data Science Data Science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

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Mlearning.ai

MAY 23, 2023

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.

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PyImageSearch

JANUARY 2, 2023

In addition to structuring data for research, machine learning (ML) can match patients to clinical trials, speed up drug discovery, and identify effective life-science therapies when applied to big data. For example, the SOPHiA GENETICS AI technology computes one genomic profile every 4 minutes.

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DrivenData Labs

MAY 1, 2025

Find, curate, or contribute data to create representative and open datasets that can be used for early prediction of AD/ADRD. Advance algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions. Interpreting model features in a real-world context is difficult with voice data.

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