Towards AI

MAY 1, 2024

We shall look at various types of machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can call their libraries in R studios, including executing the code. R Studios and GIS In a previous article, I wrote about GIS and R., Types of machine learning with R.

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Mlearning.ai

FEBRUARY 23, 2023

In this article, we’ll explore what random forests are, why they’re practical, and how to use them. Since random forests are a subset of supervised learning algorithms, they depend on labeled data. The algorithm builds a collection of decision trees and models that segment data into branches according to specific criteria.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. Let’s dig deeper and learn more about them!

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. Let’s dig deeper and learn more about them!

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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.

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Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. The global Machine Learning market was valued at USD 35.80 billion by 2031 at a CAGR of 34.20%.

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Dataconomy

MAY 25, 2023

In the subsequent sections of this article, we will explore the challenges and limitations associated with artificial intelligence in IoT, as well as the key technologies and techniques driving this convergence. These advantages have a transformative impact across various industries and domains.

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Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success.

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Pickl AI

JULY 31, 2023

Among the different learning paradigms in Machine Learnin g, “Eager Learning” and “Lazy Learning” are two prominent approaches. In this article, we will delve into the differences and characteristics of these two methods, shedding light on their unique advantages and use cases.

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Mlearning.ai

OCTOBER 2, 2023

We’ll answer exactly that question in this article. The two main categories of feature selection are supervised and unsupervised machine learning techniques. The two main categories of feature selection are supervised and unsupervised machine learning techniques. Here’s the overview.

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Mlearning.ai

AUGUST 20, 2023

Before we feed data into a learning algorithm, we need to make sure that we pre-process the data. This article will discuss the Top 4 Recommendations for building amazing training datasets. Decision Trees and Random Forests are scale-invariant. If you like this article, please clap. 2019) Python Machine Learning.

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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. 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.

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Pickl AI

FEBRUARY 7, 2025

This blog explores the difference between Machine Learning and Deep Learning , highlighting their unique characteristics, benefits, and challenges. This article aims to provide a clear comparison, helping you understand when to use Machine Learning and when to opt for Deep Learning based on specific needs and resources.

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IBM Journey to AI blog

JULY 6, 2023

Subcategories of machine learning Some of the most commonly used machine learning algorithms include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm.

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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. In this article, we will explore the essential steps involved in creating AI and the tools and techniques required to build robust and reliable AI systems. Can I create my own AI?

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Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance.

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Pickl AI

APRIL 26, 2024

It showcases expertise and demonstrates a commitment to continuous learning and growth. This article aims to guide you through the intricacies of Data Analyst interviews, offering valuable insights with a comprehensive list of top questions. Explain the difference between supervised and unsupervised learning.

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DagsHub

DECEMBER 23, 2024

They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN). Relies on explicit decision boundaries or feature representations for sample selection. This article provides a hands-on implementation of QBC in scikit-learn.

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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 Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Pickl AI

SEPTEMBER 23, 2024

Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems. The synergy between AI and Data Science enhances data-driven decision-making, powering innovations across healthcare, finance, and technology industries.

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How to Learn Machine Learning

MARCH 25, 2025

At the core of machine learning, two primary learning techniques drive these innovations. These are known as supervised learning and unsupervised learning. Supervised learning and unsupervised learning differ in how they process data and extract insights.

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