Dataconomy

AUGUST 28, 2023

How to use kernels in machine learning Kernels, the unsung heroes of AI and machine learning, wield their transformative magic through algorithms like Support Vector Machines (SVM).

Machine Learning 172

Machine Learning Machine Learning Algorithm Support Vector Machines 172

Becoming Human

MARCH 16, 2023

AI drug discovery is exploding. Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. AI has already helped identify promising candidate therapeutics, and it didn’t take years but months or even days. We will look at success stories, AI benefits, and limitations.

AI 139

AI AI Deep Learning Deep Learning 139

Dataconomy

APRIL 4, 2023

LaMDA, GPT, and more… Nowadays, everyone talking about AI models and what they are capable of. The use of AI models is expanding rapidly across all industries. AI’s capacity to find solutions to difficult issues with minimal human input is a major selling point for the technology. What is an AI model?

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

LaMDA, GPT, and more… Nowadays, everyone talking about AI models and what they are capable of. The use of AI models is expanding rapidly across all industries. AI’s capacity to find solutions to difficult issues with minimal human input is a major selling point for the technology. What is an AI model?

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Pickl AI

FEBRUARY 2, 2025

Classification: How it Differs from Association Rules Classification is a supervised learning technique that aims to predict a target or class label based on input features. Multi-itemset rules : These rules show associations among multiple items, often uncovering more complex patterns.

Data Mining 52

Data Mining Data Mining Data Mining Decision Trees 52

Towards AI

JULY 3, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. For geographical analysis, Random Forest, Support Vector Machines (SVM), and k-nearest Neighbors (k-NN) are three excellent methods. So, Who Do I Have?

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K-nearest Neighbors Machine Learning Machine Learning Algorithm 101

IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Towards AI

FEBRUARY 20, 2024

Last Updated on February 20, 2024 by Editorial Team Author(s): Vaishnavi Seetharama Originally published on Towards AI. Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms.

Machine Learning 103

Machine Learning Machine Learning ML ML 103

Pickl AI

AUGUST 8, 2024

Summary: Support Vector Machine (SVM) is a supervised Machine Learning algorithm used for classification and regression tasks. Among the many algorithms, the SVM algorithm in Machine Learning stands out for its accuracy and effectiveness in classification tasks.

Machine Learning 52

Machine Learning Machine Learning Algorithm Support Vector Machines 52

Pickl AI

SEPTEMBER 16, 2024

This section will explore the top 10 Machine Learning algorithms that you should know in 2024. Linear Regression Linear regression is one of the simplest and most widely used algorithms in Machine Learning. The agent aims to maximise the cumulative reward over time by learning a policy that maps states to actions.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

AUGUST 1, 2023

Types of Machine Learning Model: Machine Learning models can be broadly categorized as: 1. Supervised Learning Models Supervised learning involves training a model on labelled data, where the input features and corresponding target outputs are provided.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Mlearning.ai

FEBRUARY 17, 2023

Basically, Machine learning is a part of the Artificial intelligence field, which is mainly defined as a technic that gives the possibility to predict the future based on a massive amount of past known or unknown data. ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Pickl AI

JULY 12, 2024

Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence. Python’s simplicity, versatility, and extensive library support make it the go-to language for AI development. Python’s strength in AI development lies in its rich ecosystem of libraries.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

AUGUST 28, 2024

In this blog, we will delve into the world of classification algorithms, exploring their basics, key algorithms, how they work, advanced topics, practical implementation, and the future of classification in Machine Learning. What is Classification? Lazy Learners These algorithms do not build a model immediately from the training data.

Machine Learning 52

Machine Learning Machine Learning Algorithm K-nearest Neighbors 52

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.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

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. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Dataconomy

MARCH 13, 2023

The creation of artificial intelligence (AI) has long been a dream of scientists, engineers, and innovators. With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. How to create an artificial intelligence?

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Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

Pickl AI

NOVEMBER 18, 2024

Ethical considerations are crucial in developing fair Machine Learning solutions. Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. Understanding their differences helps choose the right approach for AI-driven innovations across various industries. What is Machine Learning? ML requires less computing power, whereas DL excels with large datasets.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

AUGUST 1, 2023

Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification. Gather a dataset of customer support tickets with different categories, such as billing, technical issues, or product inquiries.

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Pickl AI

JULY 31, 2023

Understanding Eager Learning Eager Learning, also known as “Eager Supervised Learning,” is a widely used approach in Machine Learning. Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data. They are handy for high-dimensional data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Pickl AI

SEPTEMBER 10, 2024

When the Perceptron incorrectly classifies an input, you update the weights using the following rule: Here, η η is the learning rate, y y is the true label, and y^ y ^​ is the predicted label. This update rule ensures that the Perceptron learns from its mistakes and improves its predictions over time.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Artificial Intelligence (AI): A branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. Association Rule Learning: A rule-based Machine Learning method to discover interesting relationships between variables in large databases.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

phData

AUGUST 24, 2023

Now that we have a firm grasp on the underlying business case, we will now define a machine learning pipeline in the context of credit models. Machine learning in credit scoring and decisioning typically involves supervised learning , a type of machine learning where the model learns from labeled data.

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets. Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Heartbeat

OCTOBER 11, 2023

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. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

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Deep Learning Deep Learning Machine Learning Machine Learning 52

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

Data Science Python Data Scientist Machine Learning 52

Pickl AI

DECEMBER 9, 2024

For example, a model may assume that similar inputs produce similar outputs in supervised learning. Algorithmic Bias Algorithmic bias arises from the design of the learning algorithm itself. How Inductive Bias Influences Model Outcomes Inductive bias directly impacts how well a model generalises to new, unseen data.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

DagsHub

DECEMBER 23, 2024

For instance, if you've developed a successful active learning process for detecting cars in self-driving applications, you can apply the same structured approach when expanding to detect pedestrians or traffic signs, since the workflow and data selection strategy are already defined and tested.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52

phData

OCTOBER 21, 2024

It is essential to understand the background of LLMs and how they fit into the broader spectrum of AI. Advancements in machine learning , alongside the computational power we’ve acquired over the years, have led to the creation of these large language models capable of processing huge amounts of data. How Do LLMs Work?

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

Dataconomy

FEBRUARY 26, 2025

Artificial intelligence (AI): It enables machines to learn from data, improving decision-making and automation. This milestone showcased the potential of machines to recognize and process complex patterns. Security systems: Analyzing patterns to detect unauthorized access or threats.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Decision Trees 91

NYU Center for Data Science

APRIL 30, 2025

Rico Angell (CDS Postdoctoral Researcher) Monitoring LLM Agents for Sequentially Contextual Harm (Building Trust WorkshopPaper) Sam Bowman (CDS Associate Professor of Linguistics and Data Science) Language Models Learn to Mislead Humans via RLHF (Poster) Inverse Scaling: When Bigger Isnt Better (Poster) Beyond the Imitation Game: Quantifying (..)

Computer Science 56

Computer Science Computer Science Data Science Supervised Learning 56