Joachim Schork’s Post

🚀 Project Demo: Anomaly Detection using Machine Learning I’m excited to share a demo video of my recent project on Anomaly Detection. In this project, I focused on identifying unusual patterns in transactional data using machine learning techniques. Such systems are highly useful in areas like fraud detection, system monitoring, and predictive maintenance. In this video, I demonstrate how the model works in practice, including how anomalies are detected and interpreted. I also deployed the whole application on the AWS instance the demo also includes accessing the application through it Tech stack: Python | Pandas | Scikit-learn | Streamlit | AWS I’d love to hear your thoughts and feedback! #MachineLearning #DataScience #AnomalyDetection #AI #Python #ProjectDemo

🤖 Top 5 Scikit-learn Codes Every Data Scientist Should Know Building a Machine Learning model doesn’t have to be complicated—if you know the right steps. With Scikit-learn, you can go from raw data to predictions in just a few lines of code. 📌 What you’ll learn: • Loading datasets • Splitting data (train/test) • Training ML models • Making predictions • Evaluating performance 💡 Mastering these fundamentals is the first step toward becoming a confident Data Scientist. Start simple. Stay consistent. Build real projects. #MachineLearning #DataScience #Python #ScikitLearn #AI #Coding #LearnToCode #TechSkills

Less noise, more substance 🕊️. We wanted to create a straightforward resource for anyone navigating the worlds of AI, Data Science, and Analytics. These pages are a reflection of our daily work and the lessons we have learned along the way. Take a look through the preview below to see what is available now. Visit us at www.codeayan.com #Codeayan #AI #DataScience #Analytics #MachineLearning #Python #GenerativeAI #AgenticAI #DataDriven #TechCommunity #WebLaunch #Coding #LLM #BigData #BusinessIntelligence #Innovation #DataStrategy #SoftwareDevelopment #TechResources #DigitalGrowth

🚀 Day 1 of My 7 Days GenAI Learning Challenge Kicking off this journey by strengthening the foundations of AI development — because great AI systems start with solid basics. 💡 Today’s Focus: Python Variables for storing AI data Lists for handling collections of data Dictionaries for structured key-value data 🧠 These may sound basic, but they are critical for: ✔️ Data handling in AI pipelines ✔️ Managing inputs/outputs efficiently ✔️ Structuring information for models ✍️ What I accomplished today: Learned core Python fundamentals Created multiple code snippets in my pynotes Wrote an article for my personal blog Sharing my learning publicly on LinkedIn ✅ 📚 Reference used: https://lnkd.in/gSdNrnjW ⏱️ Completed in just 15–60 minutes. Consistency is the real game changer. Day 1 done — let’s keep building 💪 #GenAI #Python #AIJourney #LearningInPublic #Developers #MachineLearning #BuildInPublic #CodingJourney

🚀 Day 3 of Building My AI Code Review & Bug Prediction System Today was more about actually starting the implementation. 🔹 Set up the basic backend using Flask 🔹 Started working on API structure for code input and output 🔹 Began dataset collection for training the model 🔹 Explored how to preprocess code data for ML models 💡 Key Learning: Turning an idea into a working system is challenging — but breaking it into small steps makes it manageable. 📌 Next Step (Day 4): - Complete dataset preprocessing - Train a basic ML model - Connect backend with frontend Slowly turning this idea into reality 🚀 #AI #MachineLearning #Python #Flask #BackendDevelopment #StudentProject #LearningInPublic #ECE

🏆Excited to share my latest work on Machine Learning & Al Practicals! I've created a collection of hands-on Jupyter Notebooks covering core ML concepts and algorithms as part of my academic learning journey. This project helped me strengthen my understanding by implementing models from scratch and analyzing real datasets. Key topics covered: DataFrame Operations Correlation Matrix Normal Distribution Simple Linear Regression Logistic Regression Decision Trees (ID3 Algorithm) Confusion Matrix Decision Tree Pruning Tools & Technologies: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Jupyter Notebook Through this project, I gained practical experience in: Data preprocessing Model building & evaluation Data visualization Understanding ML algorithms in depth Check out my GitHub repository: https://lnkd.in/gJCenmxd I'm continuously learning and exploring more in the field of AI & ML. Open to feedback and suggestions! #Machine Learning #ArtificialIntelligence #DataScience #Python #LearningJourney #GitHub #Students #AI #ML

Most of us learned Z-scores in school. Very few of us actually understood what they were saying. A Z-score is not just a formula. It is a question your data is asking and once you hear it, you cannot unhear it. In my latest article on Towards AI, I break down: → What a Z-score is really measuring → Why raw numbers lie without context → Where Z-scores silently power ML pipelines, anomaly detection and fraud systems → And the mistake most people make when using them. >No textbook definitions. >No dry formulas. >Just the intuition that makes it click.🎯 Link in the comments 👇 #DataScience #Statistics #MachineLearning #Python #TowardsAI #Zscore #DataAnalytics

Most people overcomplicate AI… but the foundation is actually simple. You don’t need to learn everything to get started. You just need to focus on the skills that actually matter 👇 👉 Python – the language behind most AI tools 👉 Data understanding – because AI runs on data 👉 Logic & problem-solving – the real game changer That’s it. The mistake most beginners make? Jumping into advanced topics without mastering the basics. And that leads to confusion… and quitting. At Algo Academy, we simplify this journey — so you build strong fundamentals first, then grow step by step into AI. Because strong basics = long-term success. 💬 Which skill are you focusing on right now? 📌 Save this post to come back later 📩 DM “SKILLS” if you want a clear roadmap #LearnAI #Python #DataScience #FutureSkills #Upskilling #algoacademy

Just finished Anthropic’s Introduction to Model Context Protocol — definitely worth the time. Learned how MCP lets AI models like Claude connect with tools and data without messy custom integrations. Implementing the three core building blocks — tools, resources, and prompts — using Python was a great hands-on experience. It’s free on Anthropic’s learning portal. If you’re into building smarter AI workflows, it’s a great place to start. #MCP #Anthropic #Python #AI #LLM #DeveloperTools #ContinuousLearning

Day 5 of my Machine Learning Journey 🚀 Today I worked on one of the most important concepts in data preprocessing — Encoding & Feature Scaling. 🔹 Converted categorical data into numerical using LabelEncoder 🔹 Applied Standardization using StandardScaler 🔹 Applied Normalization using MinMaxScaler 🔹 Practiced on multiple datasets (COVID, Tips, Insurance) Understanding how to properly prepare data is crucial before applying any ML model. This step directly impacts model performance. Learning step by step and building strong fundamentals 💪 #MachineLearning #DataScience #Python #LearningJourney #DataPreprocessing #AspiringDataScientist