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

Several algorithms are available, including decision trees, neural networks, and support vector machines. Train the AI system: Use the collected data to train the AI system. This involves feeding the algorithm with data and tweaking it to improve its accuracy.

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Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. What is the Central Limit Theorem, and why is it important in statistics?

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Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. They are handy for high-dimensional data.

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Dataconomy

JUNE 13, 2023

These may include machine learning algorithms like neural networks, decision trees, support vector machines, or reinforcement learning. The chosen algorithms are then trained using the prepared data to learn patterns, make predictions, or generate recommendations.

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. 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

Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

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Heartbeat

OCTOBER 25, 2023

e) Big Data Analytics: The exponential growth of biological data presents challenges in storing, processing, and analyzing large-scale datasets. Traditional computational infrastructure may not be sufficient to handle the vast amounts of data generated by high-throughput technologies.

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Heartbeat

MARCH 21, 2023

Hinge Losses  — Another set of losses for classification problems, but commonly used in support vector machines. Sometimes I can feel like things are going to be super fancy or complicated, like when “big data” was first becoming a popular buzzword, but then, when I take a look myself, it’s less scary than I thought.

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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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ODSC - Open Data Science

JANUARY 7, 2025

Scala is worth knowing if youre looking to branch into data engineering and working with big data more as its helpful for scaling applications. Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus.

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Pickl AI

APRIL 9, 2025

The Rise of Deep Learning While the concepts behind neural networks aren’t new, Deep Learning has experienced explosive growth in the last decade or so due to a confluence of factors: Big Data DL models thrive on vast amounts of labelled data for training.

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Heartbeat

OCTOBER 11, 2023

Model Complexity Machine Learning : Traditional machine learning models have fewer parameters and a simpler structure than deep learning models. They typically rely on simpler algorithms like decision trees, support vector machines, or linear regression.

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Pickl AI

SEPTEMBER 12, 2024

Association Rule Learning: A rule-based Machine Learning method to discover interesting relationships between variables in large databases. B Big Data : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis.

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Pickl AI

SEPTEMBER 23, 2024

Several technologies bridge the gap between AI and Data Science: Machine Learning (ML): ML algorithms, like regression and classification, enable machines to learn from data, enhancing predictive accuracy. Big Data: Large datasets fuel AI and Data Science, providing the raw material for analysis and model training.

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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. Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools.

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Mlearning.ai

MAY 23, 2023

Another example can be the algorithm of a support vector machine. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. What are Support Vectors in SVM (Support Vector Machine)?

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DrivenData Labs

MAY 1, 2025

Some participants combined a transformer neural network with a tree-based model or support vector machine (SVM). For more practical guidance about extracting ML features from speech data, including example code to generate transformer embeddings, see this blog post !

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