Towards AI

AUGUST 16, 2023

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. 85% or more of AI projects fail [1][2].

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AWS Machine Learning Blog

FEBRUARY 25, 2025

For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module. The following figure illustrates the F1 scores for each class plotted against the number of neighbors (k) used in the k-NN algorithm. The SVM algorithm requires the tuning of several parameters to achieve optimal performance.

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Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Unlocking the Power of KNN Algorithm in Machine Learning Machine learning algorithms are significantly impacting diverse fields.

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Ocean Protocol

FEBRUARY 2, 2023

Indeed, the most robust predictive trading algorithms use machine learning (ML) techniques. On the optimistic side, algorithmically trading assets with predictive ML models can yield enormous gains à la Renaissance Technologies… Yet algorithmic trading gone awry can yield enormous losses as in the latest FTX scandal.

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Heartbeat

FEBRUARY 20, 2023

Comet ML has an intricate web of tools that combine simplicity and safety and allows one to not only track changes in their model but also deploy them as desired or shared in teams. Workflow Overview The typical iterative ML workflow involves preprocessing a dataset and then developing the model further. Big teams rely on big ideas.

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AWS Machine Learning Blog

DECEMBER 13, 2024

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. Ensemble models are becoming popular within the ML communities. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models.

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DrivenData Labs

MAY 22, 2024

Gradient-boosted trees were popular modeling algorithms among the teams that submitted model reports, including the first- and third-place winners. Also, I have 10 years of experience with C++ cross-platform development, especially in the medical imaging domain, and for embedded solutions.

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Mlearning.ai

APRIL 10, 2023

It is a popular clustering algorithm used in machine learning and data mining to group points in a dataset that are closely packed together, based on their distance to other points. To understand how the algorithm works, we will walk through a simple example. algorithm: The algorithm used to compute the nearest neighbors of each point.

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. ML-based predictive models nowadays may consider time-dependent components — seasonality, trends, cycles, irregular components, etc. — to

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. The global Machine Learning market was valued at USD 35.80

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Mlearning.ai

JULY 6, 2023

AI-generated image ( craiyon ) In machine learning (ML), a hyperparameter is a parameter whose value is given by the user and used to control the learning process. This is in contrast to other parameters, whose values are obtained algorithmically via training. Moreover, my experience has shown it to be fairly easy to set up.

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Mlearning.ai

FEBRUARY 10, 2024

Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World Problems [link] Introduction In the constantly shifting realm of machine learning, we can see that many intricate algorithms are rooted in the fundamental principles of statistics and probability. Take the Naive Bayes algorithm, for example.

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AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. Source: Equifax) Part 1 of this series discusses the most common challenges associated with the manual lending process.

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Mlearning.ai

SEPTEMBER 24, 2023

A brute-force search is a general problem-solving technique and algorithm paradigm. Figure 1: Brute Force Search It is a cross-validation technique. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. Big O notation is a mathematical concept to describe the complexity of algorithms.

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Mlearning.ai

FEBRUARY 3, 2023

Example: Think of the ML model as a robot that you want to teach how to do a specific task, like recognizing animals. Parameters are values that are learned by an ML model during the training process, while Hyperparameters are set prior to training and remain constant during the training process.

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The MLOps Blog

DECEMBER 7, 2022

Given that the whole theory of machine learning assumes today will behave at least somewhat like yesterday, what can algorithms and models do for you in such a chaotic context ? And we at deployr , worked alongside them to find the best possible answers for everyone involved and build their Data and ML Pipelines.

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Pickl AI

MAY 17, 2023

However, while working on a Machine Learning algorithm , one may come across the problem of underfitting or overfitting. Training data plays an important role in deciding the effectiveness of an ML model. Most of the time, to avoid the underfitting issue, the ML expert ends up adding too many features to it, leading to overfitting.

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Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. The growing application of Machine Learning also draws interest towards its subsets that add power to ML models. Hence, it is important to discuss the impact of feature engineering in Machine Learning.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

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Dataconomy

AUGUST 22, 2023

Today, as machine learning algorithms continue to shape our world, the integration of Bayesian principles has become a hallmark of advanced predictive modeling. Machine learning algorithms are like tools that help computers learn from data and make informed decisions or predictions. This is where machine learning comes in.

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DrivenData Labs

APRIL 10, 2024

In the Kelp Wanted challenge, participants were called upon to develop algorithms to help map and monitor kelp forests. Winning algorithms will not only advance scientific understanding, but also equip kelp forest managers and policymakers with vital tools to safeguard these vulnerable and vital ecosystems.

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Mlearning.ai

MAY 20, 2023

image from lexica.art Machine learning algorithms can be used to capture gender detection from sound by learning patterns and features in the audio data that are indicative of gender differences. Various algorithms can be employed, such as Support Vector Machines (SVM), Random Forests, Gradient Boosting, or Neural Networks.

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Heartbeat

MARCH 27, 2023

Model versioning and tracking with Comet ML Photo by Maxim Hopman on Unsplash In the first part of this article , we made a point to go through the steps that are necessary for you to log a model into the registry. So I will pick the MLPClassifier algorithm for the next model. Have you tried Comet?

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Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD. What is MLOps?

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Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. 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.

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Pickl AI

JULY 26, 2023

K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance. To mitigate variance in machine learning, techniques like regularization, cross-validation, early stopping, and using more diverse and balanced datasets can be employed.

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Mlearning.ai

JUNE 6, 2023

Democratizing Machine Learning Machine learning entails a complex series of steps, including data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation. AutoML leverages the power of artificial intelligence and machine learning algorithms to automate the machine learning pipeline.

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DrivenData Labs

JANUARY 11, 2023

As with any research dataset like this one, initial algorithms may pick up on correlations that are incidental to the task. Logistic regression only need one parameter to tune which is set constant during cross validation for all 9 classes for the same reason. Ridge models are in principal the least overfitting models.

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Mlearning.ai

NOVEMBER 23, 2023

But deep down, we know we could achieve better results with a different approach, after all in ML, there’s no one-size-fits-all solution. This simplifies the process of model selection and evaluation, making it easier than ever to choose the right algorithm for your supervised learning task.

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Heartbeat

DECEMBER 19, 2023

Selection of Recommender System Algorithms: When selecting recommender system algorithms for comparative study, it's crucial to incorporate various methods encompassing different recommendation approaches. This diversity ensures a comprehensive understanding of each algorithm's performance under various scenarios.

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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. What is Data Science?

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Pickl AI

MAY 24, 2023

List of Python Libraries and Their Uses Given below are the Python Libraries that can be identified to be important working Python Libraries used by programmers in the industry: TensorFlow It is a computational library useful for writing new algorithms involving large number of tensor operations.

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Ocean Protocol

AUGUST 8, 2023

This deployed hyperparameters tuning and cross-validation to ensure an effective and generalizable model. Describe the ML model you chose and explain why it suited this task. Explain how the ML model contributed to your analysis and supported your findings in the report.

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Dataconomy

AUGUST 15, 2023

But how can you use this scientific approach in your next ML adventure? Unlike some other machine learning algorithms that focus on a subset of the data (sparse methods), GPs incorporate information from the entire dataset to make predictions. Techniques like cross-validation can aid in making these decisions.

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. The only drawback of using a bigger model is computational cost.

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Pickl AI

SEPTEMBER 17, 2024

Machine Learning (ML) is a subset of AI that focuses on developing algorithms and statistical models that enable systems to perform specific tasks effectively without being explicitly programmed. Clustering algorithms, such as K-Means and DBSCAN, are common examples of unsupervised learning techniques.

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