Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. This can lead to fairer and more equitable credit decisions. What Does a Credit Score or Decisioning ML Pipeline Look Like?

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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.

Machine Learning 52

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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. ML algorithms and their application [table by author] Table 2. Here I wan to clarify this issue.

Machine Learning 52

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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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Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? ✔ how to reduce the complexity and computational expensiveness of ML models ? will my data help in this ?

Machine Learning 52

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Towards AI

APRIL 11, 2023

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. The algorithm can be trained on a dataset of labeled digit images, which allows it to learn to recognize the patterns in the images.

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How to Learn Machine Learning

MARCH 25, 2025

Definition of Supervised Learning and Unsupervised Learning Supervised learning is a process where an ML model is trained using labeled data. After training, the machine learning model can predict outcomes for new, unseen data. The ML algorithm tries to find hidden patterns and structures in this data.

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Pickl AI

MAY 10, 2023

ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm. Hence, the assumption causes a problem.

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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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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

APRIL 9, 2025

Introduction In today’s rapidly evolving technological landscape, terms like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are thrown around constantly. This led to the rise of Machine Learning (ML). Machine Learning is a subset of Artificial Intelligence.

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Heartbeat

SEPTEMBER 28, 2023

To address this challenge, data scientists harness the power of machine learning to predict customer churn and develop strategies for customer retention. I write about Machine Learning on Medium || Github || Kaggle || Linkedin. ? Our project uses Comet ML to: 1. The entire code can be found on both GitHub and Kaggle.

Machine Learning 52

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Pickl AI

SEPTEMBER 30, 2024

Introduction Machine Learning (ML) is revolutionising the business world by enabling companies to make smarter, data-driven decisions. As an advanced technology that learns from data patterns, ML automates processes, enhances efficiency, and personalises customer experiences. What is Machine Learning?

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Mlearning.ai

FEBRUARY 2, 2023

bag of words or TF-IDF vectors) and splitting the data into training and testing sets. Define the classifiers: Choose a set of classifiers that you want to use, such as support vector machine (SVM), k-nearest neighbors (KNN), or decision tree, and initialize their parameters.

K-nearest Neighbors 52

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. ML works with structured data, while DL processes complex, unstructured data. ML requires less computing power, whereas DL excels with large datasets. Key Takeaways ML requires structured data, while DL handles complex, unstructured data.

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Pickl AI

JULY 1, 2024

Machine Learning Techniques for Demand Forecasting Machine Learning (ML) offers powerful tools for tackling complex demand forecasting challenges. Decision Trees These tree-like structures categorize data and predict demand based on a series of sequential decisions.

Data Science 52

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Pickl AI

JULY 12, 2024

Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

Artificial Intelligence 52

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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.

Natural Language Processing 52

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Pickl AI

NOVEMBER 28, 2024

Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. Fundamental Programming Skills Strong programming skills are essential for success in ML.

Machine Learning 52

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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. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.

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Heartbeat

FEBRUARY 22, 2023

This article delves into using deep learning to enhance the effectiveness of classic ML models. Background Information Decision trees, random forests, and linear regression are just a few examples of classic machine-learning models that have been used extensively in business for years.

Deep Learning 52

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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. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

Machine Learning 52

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Artificial Intelligence 52

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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. What is MLOps?

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ODSC - Open Data Science

JANUARY 5, 2024

PyTorch This essential library is an open-source ML framework capable of speeding up research prototyping, allowing companies to enter the production deployment phase. Key PyTorch features include robust cloud support, a rich ecosystem of tools, distributed training and native ONNX (Open Neural Network Exchange) support.

Python 52

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Dataconomy

MAY 25, 2023

ML algorithms for analyzing IoT data using artificial intelligence Machine learning forms the foundation of AI in IoT, allowing devices to learn patterns, make predictions, and adapt to changing circumstances. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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Pickl AI

JULY 26, 2023

Variance in Machine Learning – Examples Variance in machine learning refers to the model’s sensitivity to changes in the training data, leading to fluctuations in predictions. If you are planning to excel in Machine Learning concepts, but are not familiar with it, then join the Free ML Course Online course by Pickl.AI.

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Mlearning.ai

MAY 23, 2023

Decision trees are more prone to overfitting. Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. Some algorithms that have low bias are Decision Trees, SVM, etc. character) is underlined or not.

Data Science 52

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Heartbeat

OCTOBER 11, 2023

Model Complexity Machine Learning : Traditional machine learning models have fewer parameters and a simpler structure than deep learning models. They typically rely on simpler algorithms like decision trees, support vector machines, or linear regression.

Deep Learning 52

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Mlearning.ai

FEBRUARY 15, 2023

Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. A random forest is an ensemble classifier that makes predictions using a variety of decision trees. Cambridge: MIT Press.

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ODSC - Open Data Science

JANUARY 7, 2025

As MLOps become more relevant to ML demand for strong software architecture skills will increase aswell. Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on.

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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

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Pickl AI

SEPTEMBER 3, 2023

49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales. On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. An ensemble of decision trees is trained on both normal and anomalous data.

Machine Learning 52

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Pickl AI

FEBRUARY 18, 2025

Machine Learning Approaches Machine Learning (ML) techniques automate the sentiment classification process by training models on labelled datasets. Support Vector Machines (SVM) : This method identifies optimal decision boundaries to classify sentiment effectively across various datasets.

Natural Language Processing 52

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Pickl AI

SEPTEMBER 23, 2024

Machine Learning Machine Learning (ML) is a crucial component of Data Science. ML models help predict outcomes, automate tasks, and improve decision-making by identifying patterns in large datasets. This program provides a deep dive into AI and ML concepts, supported by hands-on projects and immersion modules.

Artificial Intelligence 52

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Dataconomy

MARCH 13, 2023

A new ML method will be the driving force toward improving algorithms Regularly evaluating and refining your AI model Regularly evaluating and refining your AI model is essential for improving its accuracy and efficiency. Model selection:  Choose a model that is appropriate for the size and complexity of the data.

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