John Letteboer

Interested in Data Science, 📊 Data Visualization, 👟(Obstacle) Running, 🏋🏻‍♂️CrossFit and ☕️ Coffee #python #rstats #machinelearning #ai

[Kirk Borne @KirkDBorne]

Modern #TimeSeries #Forecasting with #Python — Industry-ready #MachineLearning and #DeepLearning time series analysis with PyTorch and PANDAS: https://t.co/TUYDe9a2jN v/ @PacktPublishing

𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
🔵Apply ML and global models to improve forecasting accuracy through practical examples

🟢Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS

🔵Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions

🟢Purchase of the print or Kindle book includes a free eBook in PDF format

[Kirk Borne @KirkDBorne]

#MachineLearning for Data Streams is imperative for digital enterprises to move at the speed of data!
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#IoT #IIoT #BigData #DataScience #AI #ML #StreamAnalytics #EdgeComputing #Edge #EdgeAI #AnomalyDetection
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★★
Get the book: https://t.co/TA53CFp3Pv
★★

[Joachim Schork @JoachimSchork]

Create clear and informative box plots with added statistical insights using ggpubr! This package makes it easy to design polished plots that effectively showcase group comparisons and data distributions.

✔️ Visualize Group Comparisons: Box plots are ideal for comparing distributions across categories, showing medians, quartiles, and outliers. Combined with dot plots, they provide a detailed view of individual data points and variability.
✔️ Comprehensive Statistical Annotations: Add statistical comparisons, such as p-values and significance brackets, directly on the plot. The example here includes results from a Kruskal-Wallis test, with pairwise comparisons displayed above to indicate where significant differences exist between groups.
✔️ Customizable Design: Adjust colors, shapes, and labels to make your plots visually appealing and easy to interpret, ensuring they convey the right message.
✔️ Seamless Integration with ggplot2: Works directly with ggplot2, letting you build on your existing plots and enhance them with statistical details without the need for complex syntax.

The visualization shown here is from the package website, demonstrating how ggpubr can create polished, publication-ready plots with detailed statistical annotations: https://t.co/Dx49IJJnv5

Ready to master ggplot2 and its powerful extensions to create stunning visualizations? Enroll in my online course, “Data Visualization in R Using ggplot2 & Friends!”

See this link for additional information: https://t.co/ztlEzoEDWv

#DataViz #Rpackage #R #Statistical #DataAnalytics #RStats #ggplot2

[SAROJ PARAJULI @Parajulisaroj16]

Sentiment analysis, a vital branch of natural language processing (NLP), is used to determine whether a given piece of text expresses a positive, negative, or neutral sentiment. https://t.co/SFLubqSLZC
#DataScience #rstats #DataScientists #statistics #MachineLearning #dataviz

[Kirk Borne @KirkDBorne]

[Excellent Book] #MachineLearning Design Patterns — Solutions to Common Challenges in Data Preparation, Model-Building, and #MLOps: https://t.co/tEaXKOTDnp
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#AI #ML #DataScience #DeepLearning #DataScientists

[Paula Moraga 🌍💻📊 @Paula_Moraga_]

rspatialdata: a collection of data sources and tutorials on downloading and visualizing spatial data with #rstats 💻🗺️📊

👉 https://t.co/Oswn7xd4Cv

[Kirk Borne @KirkDBorne]

#Python #MachineLearning By Example (4th Edition): https://t.co/3mO7oBt4gc
+
GitHub: https://t.co/XmXK8pz96h
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#MachineLearning #ML #DataScience #DataScientist
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518-page book! What you will learn:

🔵Machine learning best practices throughout data preparation and model development

🟠Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning

🟢Develop and fine-tune neural networks using TensorFlow and PyTorch

🔴Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP

🟣Build classifiers using support vector machines (SVMs) and boost performance with PCA

🟡Avoid overfitting using regularization, feature selection, and more

[Kirk Borne @KirkDBorne]

Browse @AbacusAI Case Studies at https://t.co/b6Tfk9jsjk — see how their #AI platform handles all aspects of enterprise-scale #MachineLearning & #DeepLearning

#DataScience #PredictiveAnalytics #AnomalyDetection #ComputerVision #LLMs #LLMOps #MLOps

🌟 https://t.co/xYcHc1SkeS 🌟

[Kirk Borne @KirkDBorne]

#Python #MachineLearning By Example (4th Edition): https://t.co/3mO7oBt4gc
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#AI #DeepLearning #ML #BigData #DataScience #DataScientists #Coding
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Amazing 518-page book! What you will learn:

🔵Machine learning best practices throughout data preparation and model development

🟠Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning

🟢Develop and fine-tune neural networks using TensorFlow and PyTorch

🔴Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP

🟣Build classifiers using support vector machines (SVMs) and boost performance with PCA

🟡Avoid overfitting using regularization, feature selection, and more

[SAROJ PARAJULI @Parajulisaroj16]

Matplotlib provides a wide range of functions to create different types of visualizations, such as line plots, scatter plots, bar plots, pie charts, histograms, and many more. https://t.co/KisC2p7x6Y
#DataScience #pythonprogramming #datavisualizations #statistics #Matplotlib

[Kirk Borne @KirkDBorne]

The average #MachineLearning Engineer spends 98% of their time stitching services together.
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@abacusai can take data from you, train a model, deploy it and give you an endpoint you can use right away.
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Less complexity = More productive #DataScience.
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#AI #DataScientists #ML

[Matt Dancho (Business Science) @mdancho84]

80% of data science is spent cleaning & preparing data for analysis.

This is how to do it 10X faster. Let's dive in:

[ₕₐₘₚₜₒₙ @hamptonism]

Machine Learning Algorithms:

[ₕₐₘₚₜₒₙ @hamptonism]

Time Series Decomposition

[Kirk Borne @KirkDBorne]

See 244-page PDF “Introduction to #NeuralNetworks” ➡️ https://t.co/0nNNi7f7H2
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#BigData #DataScience #AI #Algorithms #MachineLearning #DeepLearning #Mathematics #Calculus #DataScientists

[Matt Dancho (Business Science) @mdancho84]

The Number 1 Skill that helped me grow my data science career? Time Series Forecasting.

Here's 2 years of help in 2 minutes:

[Ronald van Loon @Ronald_vanLoon]

The World of #AI & #Data
via @KirkDBorne

#BigData #MachineLearning #ArtificialIntelligence

cc: @pbalakrishnarao @rtehrani @pascal_bornet

[Kirk Borne @KirkDBorne]

😎Apparently, there are quite a few impressive @OReillyMedia #MachineLearning /#AI / #DataScience books: https://t.co/uBJT14ezbT

[Bindu Reddy @bindureddy]

Regardless of all the hype RAG based chat bots are still the most common applications of LLMs by far…..

[ₕₐₘₚₜₒₙ @hamptonism]

Neural Networks: