Towards AI

APRIL 11, 2023

Hand-Written Digits This problem is a simple example of pattern recognition and is widely used in Image Processing and Machine Learning. Classification In Classification, we use an ML Algorithm to classify the digit based on its features. Support Vector Machines (SVMs) are another ML models that can be used for HDR.

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Mlearning.ai

FEBRUARY 17, 2023

A complete explanation of the most widely practical and efficient field, that nowadays has an impact on every industry Photo by Thomas T on Unsplash Machine learning has become one of the most rapidly evolving and popular fields of technology in recent years. How is it actually looks in a real life process of ML investigation?

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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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Mlearning.ai

MAY 20, 2023

image from lexica.art Machine learning algorithms can be used to capture gender detection from sound by learning patterns and features in the audio data that are indicative of gender differences. Training a Machine Learning Model : The preprocessed features are used to train a machine learning model.

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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 machine learning? This post will dive deeper into the nuances of each field.

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Pickl AI

SEPTEMBER 3, 2023

Introduction Anomaly detection is identified as one of the most common use cases in Machine Learning. The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Billion which is supposed to increase by 35.6% CAGR during 2022-2030.

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Mlearning.ai

JULY 4, 2023

One such intriguing aspect is the potential to predict a user’s race based on their tweets, a task that merges the realms of Natural Language Processing (NLP), machine learning, and sociolinguistics. With the preprocessed data in hand, we can now employ pyCaret, a powerful machine learning library, to build our predictive models.

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phData

AUGUST 24, 2023

However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning. How Does Machine Learning Impact These Models? Below are just some of the advantages provided by incorporating machine learning in credit models.

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Dataconomy

AUGUST 9, 2023

This is where the power of machine learning (ML) comes into play. Machine learning algorithms, with their ability to recognize patterns, anomalies, and trends within vast datasets, are revolutionizing network traffic analysis by providing more accurate insights, faster response times, and enhanced security measures.

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Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

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Heartbeat

AUGUST 1, 2023

If your curiosity brought you here, stick around to see what CV looks like without deep learning. Scale-Invariant Feature Transform (SIFT) This is an algorithm created by David Lowe in 1999. It’s a general algorithm that is known as a feature descriptor. One example of this can be noted in the first step of the SURF algorithm.

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Heartbeat

APRIL 17, 2023

Photo by Shahadat Rahman on Unsplash Introduction Machine learning (ML) focuses on developing algorithms and models that can learn from data and make predictions or decisions. One of the goals of ML is to enable computers to process and analyze data in a way that is similar to how humans process information.

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Mlearning.ai

JUNE 14, 2023

LLM Learning MindMap: Lucidspark Learning Large Language Models Here is a print friendly view of all the resources. Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube

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Mlearning.ai

APRIL 28, 2023

Anomaly Detection Identifying Outliers in Your Data using Statistical and Machine Learning Methods In today’s data-driven world, it’s essential to be able to identify outliers or anomalies in your data. These algorithms can learn the normal pattern of the data and identify data points that deviate significantly from this pattern.

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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. Artificial Intelligence and the Future of Humans 1.

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Dataconomy

MAY 25, 2023

Simultaneously, artificial intelligence has revolutionized the way machines learn, reason, and make decisions. On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans.

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Mlearning.ai

FEBRUARY 2, 2023

The Multi-Armed Bandit (MAB) algorithm is a type of reinforcement learning algorithm that addresses the trade-off between exploration and exploitation in decision-making. The name “Multi-Armed Bandit” is inspired by a classic gambling problem in which a gambler has to decide which of several slot machines, or “arms,” to play.

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DagsHub

JUNE 3, 2024

The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. But First, Do You Really Need to Fix Your ML Model? Read more about benchmarking ML models. How do you choose an appropriate ML model?

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. Key concepts of AI The following are some of the key concepts of AI: Data:  AI requires vast amounts of data to learn and improve its performance over time.

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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. What is machine learning?

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Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Heartbeat

MAY 22, 2023

Genetic algorithms [ 1 ] are one way to detect faces in a digital image, followed by the Eigenface technique to verify the fitness of the region of interest. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. Learn probability, testing for hypotheses, regression, classification, and grouping, among other topics.

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Heartbeat

MARCH 8, 2023

Text Categorization Text categorization is a machine-learning approach that divides the text into specific categories based on its content. To extract themes from a corpus of text data and then use these themes as features in text classification algorithms, topic modeling can be used in text classification.

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IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

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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.

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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. Figure 4: A generic workflow for developing and evaluating an ML-based liquid biopsy diagnostic (source: Ko et al.,

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Heartbeat

MARCH 6, 2024

Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We're committed to supporting and inspiring developers and engineers from all walks of life.

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Pickl AI

AUGUST 22, 2023

In the ever-evolving landscape of Machine Learning, scaling plays a pivotal role in refining the performance and robustness of models. Among the multitude of techniques available to enhance the efficacy of Machine Learning algorithms, feature scaling stands out as a fundamental process.

Machine Learning 52

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Mlearning.ai

JUNE 8, 2023

This is embedding/vector/vector embedding for this article. Use algorithm to determine closeness/similarity of points. Overview Vector Embedding 101: The Key to Semantic Search Vector indexing: when you have millions or more vectors, searching through them would be very tedious without indexing.

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Heartbeat

FEBRUARY 15, 2024

Classifier Integration: The HOG features are fed into a classifier, often a Support Vector Machine (SVM), which learns to distinguish between pedestrian and non-pedestrian patterns. This learning process enables the system to make accurate predictions. Practical Deep Learning Courses: Fast.ai waitKey(0) cv2.destroyAllWindows()

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Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. Let’s get started!

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