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. This is where machine learning comes in. What is machine learning? Machine learning algorithms help you find patterns in this data.

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Pickl AI

MAY 17, 2023

Machine learning empowers the machine to perform the task autonomously and evolve based on the available data. However, while working on a Machine Learning algorithm , one may come across the problem of underfitting or overfitting. Both these aspects can impact the performance of the Machine Learning model.

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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. Training a Machine Learning Model : The preprocessed features are used to train a machine learning model.

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Mlearning.ai

JUNE 7, 2023

Machine learning is a rapidly evolving field that provides powerful tools for data analysis and prediction. Continue reading on MLearning.ai »

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Mlearning.ai

JUNE 28, 2023

{This article was written without the assistance or use of AI tools, providing an authentic and insightful exploration of PyCaret} Image by Author ‍In the rapidly evolving realm of data science, the imperative to automate machine learning workflows has become an indispensable requisite for enterprises aiming to outpace their competitors.

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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

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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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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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]. Thus, machine learning is a subfield of AI.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Ocean Protocol

FEBRUARY 1, 2024

Challenge Overview Objective : Building upon the insights gained from Exploratory Data Analysis (EDA), participants in this data science competition will venture into hands-on, real-world artificial intelligence (AI) & machine learning (ML). You can download the dataset directly through Desights.

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Ocean Protocol

SEPTEMBER 29, 2023

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

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Mlearning.ai

SEPTEMBER 24, 2023

Figure 1: Brute Force Search It is a cross-validation technique. This is a technique for evaluating Machine Learning models. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. Running a cross-validation model of k = 10 requires you to run 10 separate models. Johnston, B.

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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. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

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Mlearning.ai

JANUARY 27, 2023

Understand Different Techniques and How to Use Them for Better Model Evaluation Photo by Kelly Sikkema on Unsplash We develop machine-learning models from data. How we do this is the subject of the concept of cross-validation. Diagram of k-fold cross-validation. Train-test split. Image by the author.

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Mlearning.ai

NOVEMBER 23, 2023

This Only Applies to Supervised Learning Introduction If you’re like me then you probably like a more intuitive way of doing things. When it comes to machine learning, we often have that one (or two or three) “go-to” model(s) that we tend to rely on for most problems. Call-To-Action Enjoyed this blog post?

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Ocean Protocol

AUGUST 8, 2023

By this, Mohammed was able to conclude there is a lag in the market reaction to users' sentiments and proceeded to build a machine-learning model that reflected the outcomes of each of the mentioned parameters. This deployed hyperparameters tuning and cross-validation to ensure an effective and generalizable model.

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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. In the following sections, we discuss the stages of the process in detail.

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Pickl AI

MAY 24, 2023

This may include for instance in Machine Learning, Data Science, Data Visualisation, image and Data Manipulation. Its modified feature includes the cross-validation that allowing it to use more than one metric. NumPy NumPy is one of the most popular Python Libraries for Machine Learning in Python.

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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. This was necessary as the registry is where a machine learning practitioner can keep track of experiments and model versions.

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Dataconomy

AUGUST 15, 2023

The Gaussian process for machine learning can be considered as an intellectual cornerstone, wielding the power to decipher intricate patterns within data and encapsulate the ever-present shroud of uncertainty. At its core, machine learning endeavors to extract knowledge from data to illuminate the path forward.

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Mlearning.ai

FEBRUARY 15, 2023

Figure 1 Preprocessing Data preprocessing is an essential step in building a Machine Learning model. Deep ensemble learning models utilise the benefits of both deep learning and ensemble learning to produce a model with improved generalisation performance. Ensemble deep learning: A review. link] Ganaie, M.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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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.

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DrivenData Labs

JANUARY 11, 2023

The results of this GCMS challenge could not only support NASA scientists to more quickly analyze data, but is also a proof-of-concept of the use of data science and machine learning techniques on complex GCMS data for future missions. All bonus prize write-ups are available alongside the winners' code in the winners' repo.

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Mlearning.ai

APRIL 11, 2023

Use the cross validation technique to provide a more accurate estimate of the generalization error. Double descent phenomenon There are two different manifestations of the double descent phenomenon in machine learning. Increase the size of training data. L1 norm and L2 norm).

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Mlearning.ai

FEBRUARY 27, 2023

Grid search utilizes cross validation too, so it is crucial to provide an appropriate splitting mechanism. Again, due to the nature of the problem we can’t just use plain k-fold cross validation. The parameter configuration that achieves the best result, will be the one to form the best estimator.

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Iguazio

JUNE 25, 2023

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in. Why Evaluate Model Performance?

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Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

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Mlearning.ai

JANUARY 29, 2024

Data Science Project — Build a Decision Tree Model with Healthcare Data Using Decision Trees to Categorize Adverse Drug Reactions from Mild to Severe Photo by Maksim Goncharenok Decision trees are a powerful and popular machine learning technique for classification tasks.

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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. DBSCAN works by partitioning the data into dense regions of points that are separated by less dense areas.

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The MLOps Blog

JUNE 6, 2023

We’re about to learn how to create a clean, maintainable, and fully reproducible machine learning model training pipeline. Bookmark for later Building MLOps Pipeline for NLP: Machine Translation Task [Tutorial] Building MLOps Pipeline for Time Series Prediction [Tutorial] Why do we need a model training pipeline?

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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. Machine learning yearning. References [1].Ng, Ng, Andrew.

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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. Many ML optimizing functions assume that data has variance in the same order that means it is centered around 0. You can refer part-I and part-II of this article.

Data Science 52

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DrivenData Labs

APRIL 10, 2024

Michal Wierzbinski ¶ Place: 2nd Place Prize: $3,000 Hometown: Rabka-Zdroj (near the city of Cracow), Poland Username: xultaeculcis Social Media: GitHub , LinkedIn Background: ML Engineer specializing in building Deep Learning solutions for Geospatial industry in a cloud native fashion.

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. What are kernels? Linear Kernels 2.

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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. Let’s get started!

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