Rio Rizki Aryanto

New Data Science Batch Starting Nov 18! Ready to step into the world of Data Science and unlock endless career opportunities? Our Data Science program is launching on November 18th, designed to equip you with cutting-edge skills in Python, Machine Learning, AI, and more. Don’t miss this chance to level up your career in Data Science! Register now to secure your spot- https://buff.ly/3By3y7Y #DataScience #CareerGrowth #MachineLearning #AI #FutureOfWork #DataScienceTraining #Upskill

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Recently, I've been thinking of about what's the correct approach when we're talking about Datan Analysis and Data Science. I believe that many of us are used to hear that if we want to be one of a kind in these fields, we should handle tools like scikit learn, keras, pytorch, pandas, scipy, pyspark, airflow and so on (that's what the people who sell courses tell us every time haha) After medidating about it, I came to the conclusion that though it's essential to have programming skills, maybe we need to focus also in soft skills in order to be able to convince the others that our analysis make sense and that we should make a specific decision. Alongside with it, there's the skill related to statistics, probability, linear algebra and calculus, wich are the foundations of the Data Science and Data Analysis. So, it's not only about python, sql, mongodb, power bi and so on. It's also about, and maybe more importantly, storytelling, sharing meaningful insights and strengthening the foundations like statistics, probability and so on #datanalysis #datascience #storytelling #python

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Getting Ready For a Career in Machine Learning? A learned friend of mine described Machine Learning as a technology invented by man to cover up our laziness. This is arguably true since Machine Learning heavily depends on human intelligence and the data we feed it to learn. As fondly called by Gen Z, ML is another facet of Artificial Intelligence, which is entirely about studying patterns and then replicating them by training a machine to automate them. This, in simpler terms, means that we human beings train machines to execute the same tasks we are expected to perform. Therefore, you may ask: what is the big deal with Machine Learning? What Do I Mean By Machine Learning? As the name implies, machines do learn, but this time they do so based on previous occurrences that they have been trained to study. They identify similar patterns and analyse different data sets of what has happened before so they may use the provided information to solve a current problem, and also make predictions of what may happen in the future. The machines can achieve this because we have fed them with huge data sets capable of revealing trending patterns. Hence, the evolution of robots in different fields such as the healthcare sector, engineering firms, transportation, and military operations. How Do You Get Started With a Career in ML? You can master Machine Learning and get started by following these simple guidelines. Prepare Your Mind. Starting a career in Machine Learning often involves playing with a lot of datasets. Thus, it is recommended that you understand it before getting into it fully. You must be able to adjust your mindset accordingly.  Select a Tool to Learn.    Numerous tools are used for Machine learning. You must get yourself familiar with any of these tools If you want to start a career in ML. Some of these tools include Anaconda, Tensor Flow, Colab, BigML, Vertex AI, etc. Get Your Hands on Datasets. The next step will be to start learning about collecting and exploring datasets. However, a problem must first be identified before proceeding to use Machine Learning to solve the problem using datasets. The data collected using statistical and data visualisation methods are generally used to train a model. Gradually, the parameters are adjusted continuously to train the model perfectly until a desirable output is received. Continuously Learn and Join ML Communities.  This is the final tip from me and honestly, the most crucial one. Several people like you are fired up to start their ML journey but end up halfway. Joining powerful and relevant communities will help you foster your career growth by putting you on a platform where like-minded individuals continually encourage and share their progress in learning and accomplishments. Thanks for reading. For more updates like this, kindly hit the follow button or visit our website. You can also follow us on Twitter, and Instagram.

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🚀 Learn CNNs with TensorFlow! 🚀 Hi everyone! 👋 Want to learn about Convolutional Neural Networks (CNNs) and how to classify images? 🌟 Check out our easy guide on building a CNN in TensorFlow to work with the CIFAR-10 dataset. Perfect for beginners and those looking to improve their skills! 🔍 In this guide: Load and prepare the CIFAR-10 dataset Build a CNN using the Keras API Train and test your model Tips to make your model better Join us and boost your deep learning skills! 💡 Read the full article here: A Simple CNN In TensorFlow: Practical CIFAR-10 Guide Link to the article: https://lnkd.in/dvrhJSvA #DeepLearning #MachineLearning #AI #TensorFlow #CNN #ImageClassification #DataScience #TechLearning

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How to choose a right graph for Data Visualization #datascience #dataanalyst #seaborn #datavis #dataviz #datavisualization

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🚀Not everything about machine learning is about Python and libraries 🚀 There's a widespread misperception in the modern world that machine learning just involves writing Python code with libraries like scikit-learn, PyTorch, and TensorFlow. But machine learning goes well beyond writing code. It is fundamentally based on mathematics, specifically probability theory, linear algebra, and statistics. 📊🧮 I frequently come across publications and individuals making 30,60-day or 90-day promises to teach AI/ML. They can teach you the fundamentals, but it takes more than that to build a strong foundation. It involves delving deeply into the underlying mathematics of the models to comprehend how concepts like distributions, optimization, and regression operate. 🌟 Real machine learning isn't about fancy models or hot subjects. The secret to using those models effectively is to comprehend the underlying ideas, and that's where mathematics' magic shines. You run the risk of becoming someone who merely speaks in platitudes rather than someone who genuinely comprehends and uses AI in practical situations if you don't understand the maths. I recently found a post by Sunil Ghimire, who wonderfully stated this. It brought back memories of my experience: I struggled with maths in school. However, the value of mathematics became clear to me when I was a college student and began learning about AI and ML. It made it easier for me to understand how arithmetic, models, and practical applications underpin everything. This change in viewpoint helped me comprehend machine learning better and also helped me do well in my maths classes during my BTech. 🎓✍️ Key takeaway: Don't be afraid to tackle the arithmetic involved to succeed in AI/ML. Accept it! It holds the secret to achieving achievement and real understanding. 🔑✨ #MachineLearning #AI #Statistics #MathInML #DataScience #RealWorldAI #ContinuousLearning

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Hi everyone! 👋 Let’s explore the key differences between a Data Scientist and a Data Engineer in our quick discussion. 🔍 Data Scientist: Focuses on machine learning, AI algorithms, and building specialized models to extract valuable insights from data. Prioritizes maintaining clean data sets to ensure accurate analysis. Proficient in R or Python for statistical analysis and model building. Excels in interpersonal communication, team building, and making an impact in the boardroom with data-driven insights. 💻 Data Engineer: Specializes in software development and is responsible for building and maintaining data pipelines as well as databases and processing systems. Skilled in programming languages like Java and Python to ensure seamless data flow and system functionality. Works with big data tools such as MapReduce, Hadoop, Hive, Spark, and relational database management systems like MySQL. Thrives in team-oriented environments, is adept at team building, and is comfortable switching between different technologies to meet business needs. In essence, while Data Scientists focus on analyzing and interpreting data to drive decisions, Data Engineers build the infrastructure that makes this analysis possible. Both roles are crucial in the data ecosystem, working together to turn raw data into actionable insights! 🚀 #DataScience #DataEngineering #MachineLearning #AIAlgorithms #DataPipelines #DataAnalysis #BigData #DataScientist #DataEngineer #PythonProgramming #JavaProgramming #Hadoop #Spark #RProgramming #DataManagement #DataVisualization #SoftwareDevelopment #DataProcessing #TechRoles #DataEcosystem #DataDriven #TechCareers #TeamBuilding #AI #DataInsights #DataInfrastructure #BigDataTools #DatabaseManagement #NoSQL #MapReduce #Hive #MySQL #DataAnalytics #TechSkills #MLAlgorithms #DataModels #AIDevelopment #TechCommunity #ProgrammingLanguages #DataPipeline #TeamOriented #BoardroomPresence #TechSwitching #InterpersonalSkills #DataTechnology #DataSystems #DataSets #DataTech #TechIndustry #USITStaffing #ITRecruitment #BenchSales #StaffingSolutions #TechRecruitment #TalentAcquisition #ITJobs #RecruitingExperts #TechHiring #USStaffing #StaffingAgency #BenchSalesExperts #JobPlacement #ITConsulting #TechTalent #RecruitmentAgency #StaffingFirm #HiringTechTalent #TechStaffing #TalentManagement #StaffingNeeds #ITRecruitmentAgency #ITBenchSales #RecruitmentStrategies #TechIndustryJobs #RecruitmentSolutions #StaffingExperts #USRecruitment #JobMarket #BenchRecruitment #ITStaffingSolutions #TalentSearch #CareerOpportunities #USITJobs #StaffingProfessionals #ITJobMarket #TechCareers #TalentSourcing #JobOpportunities #RecruitmentLife #StaffingAndRecruitment #ITConsultingServices #RecruitingLife #JobPlacementServices #TalentAcquisitionStrategy #BenchSalesStrategies #ITStaffingExperts Ravindranath Konda Saini Vinod Shiva Kumar ✪ Harideep Reddy✔️ Nagarjuna G Sairaj Bandari Riya Khan Leenath Mandava Sreenath Kota Varsha Katkam Sri Ranga

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📢Hi folks, Today I have tried the counting and analytics perspectives bar chart and also analyzed the performance of YOLOV8n and YOLOV10n. Yolov10n is good and comparable to yolov8 in both latency and performance. ❓️WHAT & WHY YOLOV10 special (Ultralytics) YOLOv10 introduces a groundbreaking approach to real-time object detection, eliminating the need for non-maximum suppression (NMS) and optimizing model components for superior performance. By leveraging consistent dual assignments and a holistic efficiency-accuracy driven model design, YOLOv10 achieves state-of-the-art accuracy with reduced computational overhead. Its architecture includes enhanced backbone and neck components, along with innovative one-to-many and one-to-one heads. With model variants catering to different application needs, YOLOv10 sets new standards in accuracy and efficiency, outperforming previous YOLO versions and other contemporary detectors. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance. 💡My observations show that the Yolov10 estimation resolved a lot of issues. I'm currently attempting to do sports analytics and traffic control, the fine estimate for traffic officers and it solved a lot of real-time problems. 😇Many business-based use cases lot in this approach example detection, segmentation, bottle speed and distance estimation, in my opinion, were connected to and relied upon the Yolov10 👨‍💻GitHub (My Code): https://lnkd.in/g682M_nv 📃Yolov10 paper: https://lnkd.in/gH6bDY-G 🤔If you want to learn more about computer vision, AutoML, and basic audio processing, check out my articles. 📸3D-CV: https://lnkd.in/g5dZQuMU 📸CV Articles: https://lnkd.in/gQSswkEU 🎶Audio Basics Article: https://lnkd.in/gUbxubMR ⚙️Automl : https://lnkd.in/geCkmWwQ 📃However, I adore working on these 3D creations using AI. Connect all 3D AI, RL, Computer vision, time series, and NLP enthusiasts. 👉Thank you for coming, everyone. If you have any questions, please leave a comment. #python #pythondeveloper #deeplearning #machinelearning #aicommunity #datascientist #opencv #computervision #3dmodelling #mesh #pythonprogramming #nvidia #3d #threejs #3dimaging #imageprocessing #kagglecompetition #googledeveloperstudentclubs #artificialintelligence #animation #mesh #texture #3dmodelling #ai #ml #dl #depthmap #sam #segmentation #meta #yolov8

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[Interquartile Range] Here is a quick way to spot outliers in your data Using this Python code for IQR (Interquartile Range). The IQR is a simple way to catch anomalies, especially in skewed data. With this code, you can detect outliers quickly and keep your analysis accurate! #DataScience #Python #DataAnalytics #MachineLearning #OutlierDetection #DataTips

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🌟My First and Very Simple GenAI Project!🌟 A Retrieval-Augmented Generation (RAG) chatbot designed to provide concise responses from a collection of PDF documents. This project utilized 10 PDFs related to Machine Learning, providing a robust dataset for testing and development. Technical Implementation: - 📄 For extracting PDF data: PyMuPDF - 📚For chunking and embedding: Sentence Transformer - 🔍For retrieval: FAISS - 🤖LLM used for summarization: BART Challenges Faced: - One significant challenge I encountered was that the model occasionally generated outputs even when the data wasn't trained on a specific topic. This is an area I plan to address and improve in future iterations of the project. What's Next? I aim to: - 🛠️Enhance the preprocessing steps for better handling of tables and figures. -🎯 Fine-tune the summarization model for specific domains to improve response accuracy. Recommendations and Feedback: I would appreciate any recommendations or feedback regarding this project. Your insights would be incredibly valuable as I look to enhance and refine this chatbot further. #GenAI #NLP #MachineLearning #AI #DataScience #Chatbot #Innovation #LearningJourney Here is a glimpse of the chatbot response

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Let's have a look at the types of #Recommender Systems #RecSys

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Sharing my thoughts on random vs. time-series splits in machine learning problems with a time-series component. This topic sometimes appears in my feed, and some shared recommendations can be misleading for new data scientists who may not yet understand the reasoning behind them. My TL;DR: Don’t overcomplicate things; stick to the basics. If your problem has a time component, treat it as a time-series problem and perform a time-series split. PS: I didn't want to go into detail but normally we want to discard recent data for fraud detection because chargebacks can take up to 3 months. This also happens for other machine learning problems. May talk about this in the future .

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Guess what? Time for a new Ryoko AI Tips! 😃 Today, I share with you a very simple and useful example to understand the basic principle of Logistic Regression for classification purposes. I really hope you enjoy this entry! #AI #regression #ML #datascience #logisticregression #classification

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CodSoft TASK 1 : 🚢 Titanic Survival Prediction Project Embark on a data-driven journey through history with my Titanic Survival Prediction project! Using machine learning algorithms and the infamous Titanic dataset, I set out to uncover the factors that determined who survived the tragic sinking of the RMS Titanic in 1912. 📊 Objective: Predicting passenger survival based on various features like age, gender, class, and more. This project not only delves into the intricacies of the Titanic disaster but showcases the power of data analytics and machine learning in uncovering hidden patterns. GitHub :https://lnkd.in/edUHJAtX CodSoft #codsoft #python #internship

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💥Stock Price Prediction Sarima & Arima Code Link: https://lnkd.in/gfhHTn2e 📈 ARIMA (Auto Regressive Integrated Moving Average) ARIMA is a popular statistical method for time series forecasting. It's particularly useful when dealing with data that exhibits trends and seasonality. Components of ARIMA 1. Autoregression (AR): Uses past values of the time series to predict future values. 2. Integration (I): Transforms the time series to make it stationary (i.e., mean and variance remain constant over time). 3. Moving Average (MA): Uses past forecast errors to predict future values. 📈 SARIMA (Seasonal AutoRegressive Integrated Moving Average) SARIMA is an extension of ARIMA that explicitly accounts for seasonal patterns in the data. This makes it ideal for stock prices, which often exhibit seasonal variations due to factors like quarterly earnings reports or economic cycles. Components of SARIMA In addition to the AR, I, and MA components of ARIMA, SARIMA includes: 1. Seasonal Autoregression (SAR): Models the relationship between a value and past seasonal values. 2. Seasonal Differencing (SD): Makes the seasonal component stationary. 3. Seasonal Moving Average (SMA): Uses past seasonal forecast errors to predict future values. 1. Model Selection: - ARIMA: Use if the data doesn't exhibit significant seasonal patterns. - SARIMA: Use if the data has clear seasonal components. 2. Model Fitting: Fit the chosen model to the historical data, estimating the parameters (p, d, q) for ARIMA or (p, d, q)(P, D, Q)s for SARIMA. 3. Model Evaluation: Assess the model's performance using metrics like Mean - Squared Error (MSE), Mean Absolute Error (MAE), or Root Mean Squared Error (RMSE). 4. Forecasting: Use the fitted model to generate predictions for future stock prices. Important Considerations - Data Quality: Clean and accurate data is crucial for reliable predictions. - Model Selection: Choose the appropriate model based on the characteristics of your data. - Parameter Tuning: Optimize the model's parameters for the best possible performance. - Model Validation: Use techniques like cross-validation to assess the model's generalization ability. - Uncertainty: Stock price prediction is inherently uncertain. Consider using confidence intervals to quantify the uncertainty in your forecasts. #TimeSeries #MachineLearning #GenerativeAI #DeepLearning #DataScience #Forecasting  #ARIMA #SARIMA #Prophet #XGBoost #GANs #BigData #AI #TechInnovation

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Recently I wanted to brush up my knowledge regarding machine learning engineering, and the book of the same name by Andriy Burkov is a great choice. It covers the full life cycle of a data science project from goal definition, data collection, and data prep to model deployment and maintenance. If you are already familiar with machine learning and deep learning algorithms, Machine Learning Engineering won't iterate these topics but instead offer best practices around feature engineering and model training processes. One of the most useful content for me in the book: - Feature Engineering in Chapter 4 - How to prevent data leakage, a topic Andriy points out that can happen throughout the data science lifecycle - Model Training Chapters 5 and 6 - Model Deployment Chapter 8 Some of the techniques that are rarely mentioned but might be useful are Adversarial Validation, Model Calibration, or Neuron Coverage Thank you Andriy Burkov for this amazing book!

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🔍 New article on Medium: Variance and Standard Deviation 🔍 In my previous articles, I talked about the mean, median, and types of variables. Now, I want to invite you to explore two key statistical concepts with me that go beyond central tendency: variance and standard deviation. These two metrics will help you better understand data dispersion and how we can use this information to make more solid analyses. 📊 And the best part: a real-life example applied to investment returns! If you’ve ever wondered how to measure volatility or risk in an investment fund, this post is for you. 🔎 📥 Check out the full article. #DataScience #Statistics #Variance #StandardDeviation #Investments #Learning

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