Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s dig deeper and learn more about them!

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Pickl AI

JULY 12, 2024

Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed.

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Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. Support-vector networks. References [1] Cortes, C., & Vapnik, V. Why is it important? — Medium

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IBM Journey to AI blog

JULY 6, 2023

What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.

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Heartbeat

OCTOBER 25, 2023

Machine Learning Tools in Bioinformatics Machine learning is vital in bioinformatics, providing data scientists and machine learning engineers with powerful tools to extract knowledge from biological data. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 52

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Dataconomy

MAY 25, 2023

ML algorithms for analyzing IoT data using artificial intelligence Machine learning forms the foundation of AI in IoT, allowing devices to learn patterns, make predictions, and adapt to changing circumstances. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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Pickl AI

SEPTEMBER 23, 2024

Machine Learning Machine Learning (ML) is a crucial component of Data Science. It enables computers to learn from data without explicit programming. ML models help predict outcomes, automate tasks, and improve decision-making by identifying patterns in large datasets.

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Heartbeat

OCTOBER 11, 2023

Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach. Model Complexity Machine Learning : Traditional machine learning models have fewer parameters and a simpler structure than deep learning models.

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phData

OCTOBER 21, 2024

During training, LLMs learn statistical relationships within the text and can generate human-like responses on an endless range of topics. At its core, machine learning is about finding and learning patterns in data that can be used to make decisions. How Do LLMs Work?

Machine Learning 52

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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.

Data Science 52

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Dataconomy

MARCH 13, 2023

There are several types of AI algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Algorithm selection:  Choose algorithms that are less prone to biases, such as decision trees or support vector machines.

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Mlearning.ai

JANUARY 31, 2023

left: neutral pose — do nothing | right: fist  — close gripper | Photos from myo-readings-dataset left: extension  — move forward | right: flexion  — move backward | Photos from myo-readings-dataset This project uses the scikit-learn implementation of a Support Vector Machine (SVM) trained for gesture recognition.

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