Shap Library Explains Machine Learning Predictions

🚀 Day 43/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 7. Train Test Split 8. Correlation 9. Feature Selection Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 52/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 4: KNN Regression Today, I explored K-Nearest Neighbors (KNN) Regression, a simple yet powerful supervised machine learning algorithm used for predicting continuous values. KNN Regression works by identifying the ‘K’ nearest data points to a given input and predicting the output as the average (or weighted average) of those neighbors. KNN is widely used in applications like recommendation systems, pattern recognition, and demand forecasting. The learning journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 42/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 5. Encoding • Label Encoding • One Hot Encoding 6. Feature Scaling • Standardization(Standardization()) Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

Exploring Model Evaluation and Optimization in Machine Learning In this presentation, I explored two important concepts used in building reliable machine learning models: Cross Validation and Hyperparameter Tuning. Cross Validation helps evaluate a model’s performance by splitting the dataset into multiple folds and testing the model across different training and testing sets. This provides a more reliable estimate of how the model will perform on unseen data. Hyperparameter Tuning focuses on selecting the best parameter values that control how a model learns. Techniques such as Grid Search and Random Search are commonly used to identify the optimal configuration and improve model performance. Understanding these techniques is essential for building models that generalize well and deliver accurate predictions. #MachineLearning #DataScience #ModelEvaluation #CrossValidation #HyperparameterTuning #Python #ScikitLearn

𝗧𝗵𝗶𝘀 𝗜𝘀 𝗡𝗼𝘁 𝗝𝘂𝘀𝘵 𝗔 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 We built something more complex than a memory layer. You get a subconscious mind. You can use this with AI, Python, and machine learning. - AI helps you process data - Python helps you build the system - Machine learning helps you improve it Source: https://lnkd.in/g7mHqh5N Optional learning community: https://t.me/GyaanSetuAi

Python becomes much easier when you focus on the right areas—building GUI applications with Tkinter, exploring data science using NumPy, Pandas, Matplotlib, Seaborn, SciPy, Plotly, Bokeh, and Dask, and stepping into artificial intelligence with OpenCV, OpenAI, and Scikit-learn. Start simple, stay consistent, and you’ll gradually turn concepts into real skills. #python #coding #datascience #ai #learnpython #programming #pherochainai

🚀 Day 51/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 3: Support Vector Regression (SVR) Today, I explored Support Vector Regression (SVR), a powerful supervised machine learning algorithm used for predicting continuous values. SVR works by finding the best-fit line (or hyperplane) that not only fits the data but also keeps the prediction error within a defined margin (epsilon). It focuses on maintaining a balance between model complexity and prediction accuracy. SVR is widely used in applications like stock price prediction, demand forecasting, and time-series analysis. The learning journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 41/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 3. ML pipeline 4. Data preprocessing Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

📊 Diving into Linear Regression! Linear Regression is one of the most fundamental algorithms in Machine Learning, used to predict continuous values like housing prices, sales, and more. 🔍 What I learned: ✔️ Understanding the relationship between variables ✔️ Building prediction models in Python ✔️ Evaluating model performance using metrics 💡 It’s amazing how a simple line can uncover powerful insights from data! Currently practicing real-world problems like predicting housing prices 🏡 #MachineLearning #DataAnalytics #Python #LearningJourney #LinearRegression #DataScience

🚀 Day 1: NumPy? Today I started learning NumPy, one of the most important libraries in Python for numerical computing. NumPy allows us to work with large datasets using arrays instead of traditional lists. It is faster, more efficient, and widely used in data science, machine learning, and AI. 💡 Key takeaway: NumPy improves performance and makes complex calculations simple. #Python #NumPy #DataScience #LearningJourney