Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

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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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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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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

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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Mlearning.ai

FEBRUARY 15, 2023

Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same. K-Nearest Neighbou r: The k-Nearest Neighbor algorithm has a simple concept behind it.

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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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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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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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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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Data Science Dojo

AUGUST 24, 2023

ML models have grown significantly in recent years, and businesses increasingly rely on them to automate and optimize their operations. However, managing ML models can be challenging, especially as models become more complex and require more resources to train and deploy. What is MLOps?

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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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Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Let’s use those fancy algorithms to make predictions from our data. Later we will use another algorithm as well to see if we can further improve the result.

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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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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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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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Mlearning.ai

APRIL 11, 2023

Use the cross validation technique to provide a more accurate estimate of the generalization error. This phenomenon was observed through some algorithms such as linear regression and neural networks [4] and remains an active area of research in the field of Machine Learning/Deep Learning. Increase the size of training data.

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The MLOps Blog

JUNE 6, 2023

Complete ML model training pipeline workflow | Source But before we delve into the step-by-step model training pipeline, it’s essential to understand the basics, architecture, motivations, challenges associated with ML pipelines, and a few tools that you will need to work with. It makes the training iterations fast and trustable.

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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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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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Mlearning.ai

JANUARY 29, 2024

After reading this post, you’ll have a solid understanding of the core decision tree algorithm and how to effectively leverage it for multi class classification problems. Decision Tree Model Pipeline In the decision tree model, grid search cross-validation was utilized to determine the optimal parameters.

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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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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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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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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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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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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

JUNE 27, 2023

Originally used in Data Mining, clustering can also serve as a crucial preprocessing step in various Machine Learning algorithms. By applying clustering algorithms, distinct clusters or groups can be automatically identified within a dataset. The optimal value for K can be found using ideas like Cross Validation (CV).

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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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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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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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Dataconomy

SEPTEMBER 1, 2023

The time has come for us to treat ML and AI algorithms as more than simple trends. We are no longer far from the concepts of AI and ML, and these products are preparing to become the hidden power behind medical prediction and diagnostics. The decision tree algorithm used to select features is called the C4.5

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phData

AUGUST 1, 2023

The platform accomplishes this by using a combination of no-code visual tools, for your code-averse analysts, and code-first options, for your seasoned ML practitioners. Prophet is a time-series forecasting algorithm developed by Facebook that is popular in the data science community because of its performance and ease of use.

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phData

AUGUST 1, 2023

The platform accomplishes this by using a combination of no-code visual tools, for your code-averse analysts, and code-first options, for your seasoned ML practitioners. Prophet is a time-series forecasting algorithm developed by Facebook that is popular in the data science community because of its performance and ease of use.

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Heartbeat

MAY 29, 2023

The ML process is cyclical — find a workflow that matches. Check out our expert solutions for overcoming common ML team problems. BERT model architecture; image from TDS Hyperparameter tuning Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning algorithm.

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Mlearning.ai

JANUARY 31, 2023

Control algorithm. It provides an out-of-the-box implementation of Madgwick’s filter , an algorithm that fuses angular velocities (from the gyroscope) and linear accelerations (from the accelerometer) to compute an orientation wrt the Earth’s magnetic field. Depending on the context, this assumption may be too optimistic.

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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. This is often done using techniques such as cross-validation or grid search.

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