Smart Data Collective

JUNE 7, 2019

For centuries before the existence of computers, humans have imagined intelligent machines that were capable of making decisions autonomously. At the early era of Artificial Intelligence, programmers tried to teach machines from the definition of logical rules that the machine itself could extend during the execution of the program.

Machine Learning 101

Machine Learning Machine Learning Supervised Learning Support Vector Machines 101

Towards AI

MARCH 17, 2024

First bringing together conflicting literature on what XAI is and some important definitions and distinctions. The current state of explainability … explained Any research on explainability will show that there is little by way of a concrete definition. Interpretability — Explaining the meaning of a model/model decisions to humans.

AI 105

AI AI Decision Trees Artificial Intelligence 105

Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Towards AI

SEPTEMBER 10, 2023

This one is definitely one of the most practical and inspiring. So you definitely can trust his expertise in Machine Learning and Deep Learning. So you definitely can trust his expertise in Machine Learning and Deep Learning. Lesson #5: What ML algorithms to use Nowadays, there are a lot of different ML techniques.

Deep Learning 124

Deep Learning Deep Learning ML ML 124

Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Key Takeaways Entropy measures randomness, guiding algorithms to make better decisions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 52

Towards AI

FEBRUARY 20, 2024

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. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

Machine Learning 104

Machine Learning Machine Learning ML ML 104

Mlearning.ai

APRIL 15, 2023

The Last Dinner, Leonard Da Vinci, 1494–1498 Data structures used in algorithms As mentioned in the previous article, the right data structures are required for the performance of an algorithm to operate. An incorrect structure could prove to be detrimental or unsustainable to an algorithm. They are numbers arranged linearly.

Algorithm 52

Algorithm Python Decision Trees Computer Science 52

Pickl AI

APRIL 16, 2025

It guides algorithms in testing assumptions, optimizing parameters, and minimizing errors. hypothesis form the foundation for diverse applications, from predictive analytics and recommendation engines to autonomous systems, enabling accurate, data-driven decision-making and improved model performance.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Mlearning.ai

JULY 24, 2023

This article will cover the basics of Decision Trees and XGBoost, and demonstrate how to implement the latter for classifying celestial objects in the night sky as either a Galaxy, Star, or Quasar. Photo by Nathan Anderson on Unsplash Brief Intro to ML Algorithms A decision tree is a widely used algorithm in machine learning.

Decision Trees 52

Decision Trees Algorithm Data Scientist ML 52

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. 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

Dataconomy

APRIL 4, 2024

From deterministic software to AI Earlier examples of “thinking machines” included cybernetics (feedback loops like autopilots) and expert systems (decision trees for doctors). A lot : Some algorithmic advances have lowered the cost of AI by multiple orders of magnitude. When the result is unexpected, that’s called a bug.

Algorithm 103

Algorithm AI AI Decision Trees 103

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. , you already know that our approach in this series is math-heavy instead of code-heavy.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. AI algorithms may produce inaccurate or biased results without clean, relevant, and representative data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions. Emphasises programming skills, understanding of algorithms, and expertise in Data Analysis.

Data Science 52

Data Science ML ML Machine Learning 52

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This one is a widely used ML algorithm that is mostly focused on capturing complex patterns within tabular datasets.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. Definition of AI AI refers to developing computer systems that can perform tasks that require human intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

ML @ CMU

MAY 12, 2023

In short, this says that the (k)-th data silo may set its own ((varepsilon_k, delta_k)) example-level DP target for any learning algorithm with respect to its local dataset. Privacy definition: There are a small number of clients, but each holds many data subjects, and client-level DP isn’t suitable.

Data Silos 130

Data Silos Algorithm ML ML 130

Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. Decision trees are more prone to overfitting.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

AWS Machine Learning Blog

JUNE 22, 2023

We used AutoGluon to explore several classic ML algorithms. The best baseline was achieved with a weighted ensemble of gradient boosted decision tree models. The ML model takes in the historical sequence of machine events and other metadata and predicts whether a machine will encounter a failure in a 6-hour future time window.

AWS 115

AWS ML ML Machine Learning 115

Pickl AI

NOVEMBER 5, 2024

These statistics underscore the significant impact that Data Science and AI are having on our future, reshaping how we analyse data, make decisions, and interact with technology. For example, PayPal uses Machine Learning algorithms to analyse transaction patterns and identify anomalies that may indicate fraudulent activity.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

MAY 30, 2024

Developing predictive models using Machine Learning Algorithms will be a crucial part of your role, enabling you to forecast trends and outcomes. This phase entails meticulously selecting and training algorithms to ensure optimal performance. You will collect and clean data from multiple sources, ensuring it is suitable for analysis.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Heartbeat

SEPTEMBER 13, 2023

Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems. It allows developers, auditors, and regulators to examine the decision-making processes of the models, identify potential biases or errors, and assess their compliance with ethical guidelines and legal requirements.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

NOVEMBER 2, 2023

These reference guides condense complex concepts, algorithms, and commands into easy-to-understand formats. Expertise in mathematics and statistical fields is essential for deciding algorithms, drawing conclusions, and making predictions. Let’s delve into the world of cheat sheets and understand their importance.

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

PyImageSearch

DECEMBER 12, 2022

We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. AdaBoos t A formal definition of AdaBoost (Adaptive Boosting) is “the combination of the output of weak learners into a weighted sum, representing the final output.” Table 1: The Dataset.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. To obtain practical expertise, run the algorithms on datasets. It includes regression, classification, clustering, decision trees, and more.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

JULY 26, 2023

In this article, we will explore the definitions, differences, and impacts of bias and variance, along with strategies to strike a balance between them to create optimal models that outperform the competition. K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Mlearning.ai

APRIL 13, 2023

Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

AWS Machine Learning Blog

OCTOBER 8, 2024

During this step, AutoMLV2 will automatically pre-process your data, select algorithms, train multiple models, and tune them to find the best solution. The following figure is a decision tree to help you decide what type of endpoint to use.

Machine Learning 98

Machine Learning Machine Learning Data Preparation AWS 98

AssemblyAI

NOVEMBER 9, 2023

It's in contrast to a really broad and undefined definition of the word ”outcome” Paul, one of the design managers at Intercom, was struggling to differentiate between customer outcomes and business impact. Taking this intuition further, we might consider the TextRank algorithm.

AI 102

AI AI Machine Learning Machine Learning 102

DataRobot

JUNE 17, 2021

Personally, I think there are definitely horizons in math, biology, and physics that we may never reach… but maybe AI can change that. This was the primary inspirations to Eureqa’s algorithm. This search for mathematical formulas makes Eureqa different from other machine learning algorithms. What to Know More about Eureqa?

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Dataconomy

MARCH 28, 2025

Machine learning algorithms represent a transformative leap in technology, fundamentally changing how data is analyzed and utilized across various industries. What are machine learning algorithms? Outputs: The results generated by the algorithms, whether classifications, predictions, or recommendations based on the patterns identified.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Dataconomy

APRIL 16, 2025

Supervised learning is a powerful approach within the expansive field of machine learning that relies on labeled data to teach algorithms how to make predictions. Supervised learning refers to a subset of machine learning techniques where algorithms learn from labeled datasets.

Supervised Learning 91

Supervised Learning Decision Trees Algorithm Machine Learning 91

Dataconomy

MARCH 25, 2025

The decision boundary would be a line that separates these two groups, determining whether a new point falls into the cat or dog category based on its features. Definition of decision boundary The definition of a decision boundary is rooted in its functionality within classification algorithms.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Clustering 91

Dataconomy

MARCH 27, 2025

Definition and purpose Neural networks are designed to mimic human brain functions using layers of interconnected nodes, processing input data through complex mathematical computations. Symbolic approaches, such as decision trees, offer clarity and reasoning but may lack the speed and capacity of neural networks.

AI 125

AI AI Deep Learning Deep Learning 125

Dataconomy

MARCH 25, 2025

They provide a foundational understanding and a reference point from which data scientists can gauge the performance of advanced algorithms. Decision trees: Provide interpretable predictions based on logical rules. By understanding their performance, data scientists can design and refine complex algorithms effectively.

Decision Trees 91

Decision Trees Machine Learning Machine Learning Data Scientist 91

How to Learn Machine Learning

MARCH 25, 2025

It helps business owners and decision-makers choose the right technique based on the type of data they have and the outcome they want to achieve. Let us now look at the key differences starting with their definitions and the type of data they use. The ML algorithm tries to find hidden patterns and structures in this data.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59

ML @ CMU

DECEMBER 2, 2024

The focus is on understanding the trade-off between sample complexity (the number of data samples needed for learning) and communication complexity (the amount of data exchanged between agents) for intermittent communication algorithms, a commonly used approach in federated settings.

Deep Learning 230

Deep Learning Deep Learning Machine Learning Machine Learning 230