Jenifer Alejandra Aguilera Huertas

I just passed the Databricks Machine Learning Professional certification 🤓 This certification is great if you are interested in open-source tools: most of the exam is about MlFlow, SparkML, Delta, and - naturally - how it all fits together in the Databricks platform 📊 Studying for it is also quite a smooth experience: the videos and labs at the Databricks academy are super well elaborated and cover all the topics of the exam, and by complementing that with the documentation and a few books you get all you need to pass. My only criticism of Databricks is that the free version of the platform (Databricks Community Edition) is very limited, especially when it comes to machine learning. That may be fine if you are working on a project and have access to a premium/enterprise environment, but even then we can't just "play around" a customer's tenant 😢 If you want more tips about Databricks, feel free to ask or check my study annotations: https://lnkd.in/dpjPQ6Q2 #machinelearning #cloudcomputing

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Excellent article from Iván Palomares Carrascosa on Statology! For professionals without a profound statistics background, choosing the right statistical test to perform on their data might sometimes not be a straightforward task. This article provides a decision tree-based guide aimed at helping them navigate the problem of choosing the right test depending on the data and problem they are facing, and the hypothesis to be tested.

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🎉 Just completed my Python course on Kaggle! 🚀 Curious about Python's impact on Machine Learning? Let's discuss! Python's pivotal role in Machine Learning cannot be overstated. As a versatile and powerful language, it is the backbone of numerous ML projects. Here are a few reasons why Python is indispensable for Machine Learning: · Ease of learning: Python's simple syntax makes it accessible to beginners and experts alike. · Rich libraries: Extensive libraries like TensorFlow, PyTorch, and scikit-learn accelerate development. · Community support: A robust community means ample resources and support. · Integration: Seamless integration with other technologies and tools. Do you agree with me? 👍🏼 #Python #MachineLearning #AI #Programming #DataScience #Tech #Coding #DeepLearning #ArtificialIntelligence #LinkedInLearning

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🤯 I had no idea you could publish interactive data visualisations on #Medium. Whoa. This changes everything #datavisualization #plotly #datascience

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Get ready to optimize your NLP models with vLLM! In his latest blog post, Sergio Morales Esquivel explores the practicalities of streamlining NLP models for real-world efficiency. In it, you'll learn: ✅​ About the vLLM library for efficient model serving ✅​ Practical strategies for conserving computing resources during inference ✅​ The art of batched inference for seamless AI deployments Read on and discover how to make your models perform smarter, not harder: https://lnkd.in/egnUbWsk #Data #DataEngineering #DataScience #ArtificialIntelligence #NLP

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I am thrilled to announce that I am now a Databricks Certified Machine Learning Professional! 🎉 Throughout this journey, I've gained valuable skills in MLOps, MLflow, PySpark, and Databricks features. This certification has not only equipped me with the knowledge to build and manage robust machine learning models, but also the ability to deploy and monitor these models in a production environment. #Databricks #MachineLearningEngineering #DatabricksLearning

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Graph-based Euclidean clustering is reshaping point cloud segmentation by automating processes, reducing noise, and offering robust scalability for complex scenes. Dive deeper in this article by Dr. Florent POUX. #MachineLearning #Clustering

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motoring machine learning in python #python

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🚀Phyton for data science 🔭 Are you prepared to advance your knowledge in data science? 📍Join us at https://genai-training.com ✨ You are the target audience for our Python for Data Science course! 🔑 What you will discover: ▪️ Gain an understanding of control Flow concepts to effectively manage data pipelines. ▪️Use functions to automate tasks and streamline your code Construct and apply modules to efficiently organize intricate data science tasks. ✨ This course will equip you with the skills to: ✅ Handle data more efficiently . ✅ Write streamlined and reusable code. ✅ Simplify intricate data analysis processes, regardless of your level of Python knowledge. 🚀 Enroll now at https://genai-training.com to take advantage of new opportunities in the data science field! ✉️[email protected] #TechSkills #DataAnalysis #DataScience #Python #TrolFlow #Functions #Modules #CareerGrowth

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We are very pleased to announce Javier González, Principal Researcher in ML at Microsoft as an invited speaker for our #AI and #ML #SummerSchool! He will talk about the potential of AI and ML to revolutionise #ClinicalTrials. Secure your spot and hear it all: https://lnkd.in/gd5nWawe Have a read and learn why Next Generation Clinical Trials are such a big subject for us and what AI & ML can do to change the game: https://lnkd.in/esX3xrfK

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BITE SIZED DATA-SCIENCE -->> Day #4 https://lnkd.in/g94tGDFq This next video covers the np array( ) method, pertaining to single dimentional and muti-dimensional data structures. When we explore these, what is DIMENSION and SHAPE as it pertains to rows and columns?

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🏎️ Desights Formula 1 Data Challenge: Predict Pit Stop Strategies for the Mexican Grand Prix! Ready to take on the world of Formula 1 through data science? This month’s challenge focuses on predicting pit stop strategies for the upcoming 2024 Mexican Grand Prix, powered by Ocean Protocol. You'll get the chance to work with real-world F1 data from past seasons and build machine learning models to predict key race strategy components like: Number of Stints Tire Compounds Laps per Stint Average Lap Time The goal? Optimize race strategies to help teams stay ahead. In a sport where every second counts, your model could be the difference between a podium finish and a missed opportunity. 🔑 Why should you join? $10,000 prize pool for the top 10 participants 🏆 Earn points toward the 2024 Championship for even more rewards 📈 Real-world F1 data to test and improve your skills 🏎️ This challenge is perfect whether you're a seasoned data scientist or just starting out. Ready to take on the high-stakes world of F1 strategy? Submit your model before the deadline and get your engine roaring! 🚀 Join the challenge here! https://lnkd.in/eNYkz-8e #Formula1 #DataChallenge #Desightschallenge

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🚀 Just read an amazing new blog post by Tanel Poder on visualizing and comparing embedding vectors as heat maps! 🔥 If you're into data visualization or machine learning, this is a must-read. Tanel breaks down complex concepts into easy-to-understand visuals. How do you currently visualize your data? Do you think heat maps could improve your workflow? Let's discuss! 👇 #DataScience #MachineLearning #DataVisualization

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Unveiling the Power of Exploratory Data Analysis (EDA): Exploratory Data Analysis (EDA) is an essential step in data science, enabling professionals to unlock the story hidden within datasets. From detecting anomalies to uncovering relationships, EDA lays the groundwork for impactful analysis and informed decision-making. This blog dives into: The critical role of EDA in understanding data. Key objectives like identifying patterns, verifying assumptions, and spotting outliers. A practical example demonstrating EDA in action. Whether you’re preparing data for advanced modeling or seeking actionable insights, EDA is where it all begins. 📖 Dive into the theory, explore practical applications, and learn how EDA helps make sense of complex datasets. 💡 Ready to get started? Check out the full blog now! 👉 Read the Blog - https://lnkd.in/daHtyKDe

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🔍 Exploring Data Science Salaries: A Pandas Series Practice 📊 I recently worked on a practice exercise focused on exploring data science salaries using Pandas Series. This exercise is part of a learning module designed to build a solid foundation in Pandas Series operations. In this practice, I explored key aspects of the dataset, including: 🔹 Job titles 🔹 Salaries 🔹 Experience levels 🔹 Company sizes Key tasks included: ✅ Loading and inspecting the data ✅ Performing basic statistical operations ✅ Applying simple data transformations using Pandas Series This exercise provided a great opportunity to strengthen my understanding of data manipulation and basic data exploration with Pandas, setting the groundwork for more advanced projects in the future. #DataAnalysis #PandasSeries #DataScience #LearningInProgress #DataSkills

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What exactly are Knowledge Graphs, and why are they so integral to these technologies? Cristian Leo dives into it all in his newest article. #DataScience #LLM

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