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?

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Dataconomy

OCTOBER 5, 2023

If you are interested in technology at all, it is hard not to be fascinated by AI technologies. Whether it’s pushing the limits of creativity with its generative abilities or knowing our needs better than us with its advanced analysis capabilities, many sectors have already taken a slice of the huge AI pie.

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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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Towards AI

JULY 15, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. R has become ideal for GIS, especially for GIS machine learning as it has topnotch libraries that can perform geospatial computation. R has simplified the most complex task of geospatial machine learning and data science. data = trainData) 5.

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Dataconomy

MAY 25, 2023

By harnessing the power of AI in IoT, we can create intelligent ecosystems where devices seamlessly communicate, collaborate, and make intelligent choices to improve our lives. Let’s explore the fascinating intersection of these two technologies and understand how AI enhances the functionalities of IoT.

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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.

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IBM Journey to AI blog

DECEMBER 20, 2023

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions.

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Data Science Dojo

JULY 23, 2024

Tree-Based Algorithms: Algorithms like decision trees and random forests can handle label-encoded data well because they can naturally work with the integer representation of categories. You can also join our Discord community to stay posted and participate in discussions around machine learning, AI, LLMs, and much more!

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Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Introduction Machine Learning (ML) has rapidly evolved over the past few years, becoming an integral part of various industries, from healthcare to finance.

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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.

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Towards AI

JANUARY 26, 2024

Last Updated on January 29, 2024 by Editorial Team Author(s): Shivamshinde Originally published on Towards AI. Support Vector Machine Classification and Regression C: This hyperparameter decides the regularization strength. It can have values: [‘l1’, ‘l2’, ‘elasticnet’, ‘None’]. C can take any positive float value.

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Towards AI

APRIL 11, 2023

Last Updated on April 12, 2023 by Editorial Team Author(s): Surya Maddula Originally published on Towards AI. Support Vector Machines (SVMs) are another ML models that can be used for HDR. ANNs can be trained to recognize patterns in the numerical features extracted from digit images.

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Pickl AI

AUGUST 28, 2024

Examples include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks. Decision Trees Decision Trees are tree-based models that use a hierarchical structure to classify data. It is commonly used for binary classification tasks.

Machine Learning 52

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Towards AI

APRIL 14, 2023

Last Updated on April 17, 2023 by Editorial Team Author(s): Kevin Berlemont, PhD Originally published on Towards AI. Photo by Artem Maltsev on Unsplash Who hasn’t been on Stack Overflow to find the answer to a question?

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Mlearning.ai

APRIL 6, 2023

For larger datasets, more complex algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks may be more suitable. For example, if you have binary or categorical data, you may want to consider using algorithms such as Logistic Regression, Decision Trees, or Random Forests.

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Dataconomy

FEBRUARY 24, 2023

Nowadays, almost everyone wants to learn how to use AI, and it would be quite wrong to say that these requests are unreasonable. In 2022, the AI market was worth an estimated $70.9 Naturally, AI experts will get the biggest slice of the pie and the need for AI expertise is rising rapidly along with the field itself.

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Pickl AI

MAY 10, 2023

Decision Trees Decision Trees are non-linear model unlike the logistic regression which is a linear model. The use of tree structure is helpful in construction of the classification model which includes nodes and leaves. Consequently, each brand of the decision tree will yield a distinct result.

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Pickl AI

AUGUST 1, 2023

Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks. Common examples include: Linear Regression: It is the best Machine Learning model and is used for predicting continuous numerical values based on input features.

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Dataconomy

JUNE 13, 2023

By incorporating insights from psychology, cognitive science, and economics, decision models can better account for biases, preferences, and heuristics that impact decision outcomes. AI algorithms play a crucial role in decision intelligence. How does decision intelligence work?

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Mlearning.ai

MARCH 13, 2023

Photo by Andy Kelly on Unsplash Choosing a machine learning (ML) or deep learning (DL) algorithm for application is one of the major issues for artificial intelligence (AI) engineers and also data scientists. Here I wan to clarify this issue.

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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.

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Dataconomy

AUGUST 31, 2023

Machine learning models have already started to take up a lot of space in our lives, even if we are not consciously aware of it. Embracing AI systems and technology day by day, humanity is experiencing perhaps the fastest development in recent years. You want an example: ChatGPT, Alexa, autonomous vehicles and many more on the way.

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Pickl AI

JANUARY 29, 2024

The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. Markets for each field are booming, offering diverse job roles, especially in Machine Learning for Data Analytics. ML catalyses AI advancements, enabling systems to evolve and improve decision-making. billion by 2032.

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Mlearning.ai

FEBRUARY 17, 2023

But I also want truly define that ML isn’t represent some kind of unsecured AI technologies, super brain or dark magic, it’s clear combination of programming skills, enough amount of data, cloud solutions, theory of algorithms and math  — that’s all we should have to be able to work in this branch. In this article, I will cover all of them.

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Pickl AI

JULY 1, 2024

Decision Trees These tree-like structures categorize data and predict demand based on a series of sequential decisions. Random Forests By combining predictions from multiple decision trees, random forests improve accuracy and reduce overfitting. Ensemble Learning Combine multiple forecasting models (e.g.,

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Pickl AI

DECEMBER 9, 2024

In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds. Inductive bias is crucial in ensuring that Machine Learning models can learn efficiently and make reliable predictions even with limited information by guiding how they make assumptions about the data.

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Pickl AI

JULY 14, 2023

Even in the time of pandemic, AI has enabled in providing technical solutions to the people in terms of information inflow. Therefore, AI has been evolving since years now and is currently at its peak of development. AI has been disrupting every industry in the world today and will supposedly make larger swings in the next 5 years.

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Pickl AI

JULY 31, 2023

Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction.

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IBM Journey to AI blog

DECEMBER 19, 2023

Similar to a “ random forest ,” it creates “decision trees,” which map out the data points and randomly select an area to analyze. Isolation forest models can be found on the free machine learning library for Python, scikit-learn. Through fast and comprehensive analysis, IBM watson.ai

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Dataconomy

AUGUST 9, 2023

While network traffic analysis has traditionally involved many careful steps but today AI and ML applications have both accelerated and simplified this process ( Image credit ) Network traffic analysis is traditionally a multi-stage and complicated process.

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Pickl AI

APRIL 9, 2025

Summary: Artificial Intelligence (AI) and Deep Learning (DL) are often confused. AI vs Deep Learning is a common topic of discussion, as AI encompasses broader intelligent systems, while DL is a subset focused on neural networks. Is Deep Learning just another name for AI? Is all AI Deep Learning?

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

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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. AI models can range from simple linear models to complex neural networks.

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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.

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Pickl AI

DECEMBER 10, 2024

Model-Related Hyperparameters Model-related hyperparameters are specific to the architecture and structure of a Machine Learning model. They vary significantly between model types, such as neural networks , decision trees, and support vector machines.

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Pickl AI

OCTOBER 15, 2024

Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. At each node in the tree, the data is split based on the value of an input variable, and the process is repeated recursively until a decision is made.

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ODSC - Open Data Science

JANUARY 5, 2024

With the explosion of AI across industries TensorFlow has also grown in popularity due to its robust ecosystem of tools, libraries, and community that keeps pushing machine learning advances. As expected with the rise of AI, machine learning libraries and data science-focused libraries will become the most popular ones of 2023.

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