📊 Car Price Prediction using Linear Regression Built a simple machine learning model to understand how mileage and age impact car prices. 🔹 Used Python, Pandas, NumPy & Scikit-Learn 🔹 Performed train–test split for evaluation 🔹 Visualized the negative relationship between mileage and price Small steps, consistent learning 🚀 #MachineLearning #Python #DataScience #LinearRegression #LearningByDoing #MLBeginner

During this workshop, I learned: •Creating interactive visualizations in Python •Debugging Python code efficiently using AI •Writing optimized Python code with AI assistance This experience enhanced my practical understanding of Python and AI-driven development. Looking forward to applying these skills in real-world projects through AI for Techies #Python #ArtificialIntelligence #AI #Workshop #Learning

📊 One of the most impactful tools in my data analysis journey has been the Pandas library in Python. It has made data cleaning, filtering, aggregation, and preparation for visualization far more efficient. Applying it in real projects showed me how powerful the right tools can be in turning raw data into meaningful insights. Still learning and exploring more every day 🚀 #DataAnalysis #Python #Pandas #LearningJourney

🚀 Strengthening my Python Foundations Recently worked on core Python concepts including: • Lists, Tuples, Sets, and Dictionaries • String operations & formatting • Conditional statements • Performance comparison using timeit Focusing on building strong fundamentals before diving deeper into Data Science & AI. Consistency > Motivation 💻✨ #Python #LearningJourney #ComputerScience #FutureAI #Coding

The third reason for learning Python in football is that it is the tool which is used to build machine learning models. From expected goals and expected threat to predicting transfer values, machine learning tools are nearly all built with Python. If you want to know the future, you need to have a model of the past.

We know that creating knowledge graphs from unstructured data can be a headache. Now? The Neo4j GraphRAG for Python package includes a Knowledge Graph Builder to help you convert your unstructured and structured data. The result: Organized representations of real-world entities and relationships that power better #AI applications. Read more about this ⬆️ in the blog and learn how to create them in the #GraphAcademy course "Constructing Knowledge Graphs with Neo4j GraphRAG for Python." https://bit.ly/4pRh6iK #Python #Neo4j

Today’s Python Focus: Data Types Before building complex AI systems, you must master the basics. Today I covered: ✔ Numeric Types ✔ Strings ✔ Lists & Tuples ✔ Sets ✔ Dictionaries ✔ Type Conversion Strong foundations create strong developers. On to the next concept tomorrow 💪 #Python #FutureEngineer #LearningInPublic #AIJou

“Better features = better predictions. Here’s how I realized it.” I spent hours transforming and selecting features in my dataset. The impact was huge: • Improved model accuracy • Reduced overfitting • Revealed hidden patterns in data This taught me that algorithms matter less than meaningful features. 💬 What’s your go-to feature engineering trick? #DataScience #MachineLearning #FeatureEngineering #Python #Projects

Exploring Ensemble Learning: BaggingClassifier vs RandomForestClassifier (Python) Recently, I experimented with BaggingClassifier and RandomForestClassifier using Scikit-learn. ·      Created a dataset with make_classification ·      Trained both models ·      Visualized decision trees using plot_tree ·      Compared how Bagging uses random data while Random Forest uses random data + random features Key takeaway: Random Forest provides better generalization by reducing overfitting more effectively. This hands-on practice helped me understand ensemble learning and model interpretability at a deeper level. Always learning, always building #MachineLearning #Python #DataAnalytics #ScikitLearn #RandomForest #Bagging #LearningByDoing

Edge-Quantum Inference. Goal-oriented agents managing real-time prediction fixes on federated multi-QPU edge nodes. Skills: Python, scikit-learn. https://lnkd.in/dR837zSA #EdgeAI #QuantumInference #DataScience