Jariful Hassan’s Post

Chapter ONE of THREE !! Completed PHASE I of Machine Learning Specialization by DeepLearning.ai (Andrew Ng) This course delved deep into supervised learning and unsupervised learning, equipping me with the skills to tackle: Regression Analysis: Predicting continuous values (think house prices or sales forecasts). Classification Algorithms: Building models that categorize data (spam detection, customer churn). Beyond theory, I gained practical experience through projects, mastering: Machine Learning in Python: Utilizing libraries like NumPy and scikit-learn to build robust models. Data Wrangling: Preprocessing and feature engineering data for optimal performance. Model Evaluation: Understanding key metrics to assess model accuracy and effectiveness. #andrewng #DeepLearningAI #machinelearning #deeplearning #coursera #skillshare #datascience #cfbr #stanford

Day 5 of 100 Days of Machine Learning: Support Vector Machines 🚀 Today's focus was on Support Vector Machines (SVM), a powerful and versatile ML algorithm. 🔍 **Geometric Intuition:** Visualized how SVM separates classes using hyperplanes in a high-dimensional space. 📐 **Mathematical Derivation:** Delved into the mathematical formulation of SVM and how it optimizes the margin between data points. 🔢 **+1 and -1 Values for Support Vector Planes:** Understood why SVM uses +1 and -1 for class labels in support vector planes. 📉 **Loss Function (Hinge Loss) Interpretation:** Learned about the hinge loss function and its role in SVM. 🔄 **Dual Form of SVM Formulation:** Explored the dual form of SVM, which helps in dealing with high-dimensional data. 🔍 **Kernel Trick:** Understood the kernel trick and how it enables SVM to work in non-linear spaces. 🔢 **Polynomial Kernel:** Studied the polynomial kernel and its applications in SVM. 🌐 **RBF Kernel:** Explored the Radial Basis Function (RBF) kernel, a popular choice for non-linear classification tasks. 🔍 **Domain-Specific Kernels:** Learned about kernels tailored for specific domains and their advantages. ⏱ **Train and Run Time Complexities:** Analyzed the computational complexity of training and running SVM models. 🔄 **nu-SVM:** Examined nu-SVM, which controls the number of support vectors and errors. 📊 **SVM Regression:** Applied SVM to regression problems, expanding its utility. 📝 **Cases:** Reviewed various cases to understand the practical applications and limitations of SVM. 💻 **Code Sample:** Implemented SVM in Python, demonstrating its application and performance. SCKIT LEARN - https://lnkd.in/gwg-_JCb GFG-https://lnkd.in/g5wpUBfq Continuing to strengthen my ML knowledge and sharing my journey each day. Stay tuned for more insights! #100DaysOfML #MachineLearning #DataScience #Python

🚀 Excited to share my latest project! 🤖  I built an **Age Prediction Machine Learning Model** using **RandomForestClassifier** and **GridSearchCV**! 🎉  Here's what I accomplished:   ✅ **Data Preprocessing**: Cleaned, normalized, and prepared the dataset for analysis.   ✅ **Feature Engineering**: Extracted meaningful features to improve model performance.   ✅ **Model Training**: Implemented **RandomForestClassifier** for robust predictions.   ✅ **Hyperparameter Tuning**: Optimized the model using **GridSearchCV** for the best parameters.   ✅ **Evaluation**: Achieved high accuracy and precision in age prediction.  This project was a great learning experience in end-to-end machine learning workflows, from data processing to model optimization. 💡  #MachineLearning #DataScience #RandomForest #GridSearchCV #AI #DataPreprocessing #Python #AgePrediction #DataAnalytics

🎯 Just Completed: Feature Scaling - Standardization 🎯 I'm excited to share that I've just completed a key concept in data preprocessing: Feature Scaling using Standardization! 🚀 In data science, this technique is crucial for ensuring that each feature contributes equally to machine learning models by scaling them to have zero mean and unit variance. Here's what I covered: Why standardization is important for algorithms like SVMs, K-Means, and logistic regression. How to implement standardization using libraries like scikit-learn. Real-world applications where standardization significantly improves model performance. This concept is a game-changer for improving the accuracy of models and optimizing training time! I’m looking forward to applying this in my next project. 💻📊 #DataScience #MachineLearning #FeatureScaling #Standardization #DataPreprocessing #AI #Python #Sklearn

My Machine Learning Journey - Support Vector Machines (SVM) Hey everyone! 🚀 I recently started diving into machine learning, and one of the key concepts I've learned is Support Vector Machines (SVM). Special thanks to Krish Naik for breaking it down in such a simplified way! 🙌 Here’s what I’ve covered so far: Understanding SVM: A powerful supervised learning algorithm used for classification tasks. Linear vs. Non-linear Classification: How SVM uses hyperplanes to classify data points. Kernel Trick: I learned how SVM uses kernels like Radial Basis Function (RBF) to handle non-linearly separable data. Practical Implementation: I also implemented SVM in Python and explored how it performs on real-world datasets. This journey has been exciting, and I can’t wait to explore more advanced ML models. Stay tuned for more updates as I continue learning and growing in this space! #MachineLearning #SupportVectorMachine #AI #Python #KrishNaik Krish Naik #MLJourney #SVM #DataScience

Random Forest vs Gradient Boosting: Understanding the Differences, Similarities, and Applications! 🌳✨ Machine learning is all about choosing the right tools for the job. While Random Forest offers simplicity and speed, Gradient Boosting excels in accuracy and performance for complex tasks. For more insights on machine learning and data science, check out these awesome resources: W3Schools.com for programming fundamentals. GeeksforGeeks for detailed algorithm tutorials. Kaggle for datasets and machine learning challenges. scikit-learn for Python ML libraries. ChatGPT for content creation and exploring machine learning topics with AI. machine learning models. Discover how these algorithms stack up and where they shine. Which one do you prefer for your projects? Let’s connect and discuss! #MachineLearning #DataScience #Algorithm #RandomForest #GradientBoosting #AI #BigData #Python

One of the best books on Bayesian Data Analysis is available for free, covering key fundamentals like probability and inference, single and multiparameter models, and hierarchical models. This is a great resource to go from basics to the more advanced nuances, such as computational techniques like Markov chain Monte Carlo and Hamiltonian Monte Carlo, as well as practical deep dives across other topics like regression models and nonparametric methods. Free Book: https://lnkd.in/ebSVrSKv -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

Predicting Titanic Survival Rates with Random Forest Classifier I'm thrilled to share my latest machine learning project! Using Kaggle's Titanic dataset, I leveraged FastAI to train a Decision Tree and Random Forest Classifier, achieving an impressive 0.2078 mean absolute error. Key Insights: - Sex and fare price are pivotal factors in predicting survival - Ensemble methods significantly boost performance Technical Details: - Data preprocessing with FastAI - Feature engineering: categorical and continuous variables - Hyperparameter tuning for optimal results Results: - Decision Tree: 0.2182 mean absolute error - Random Forest: 0.2078 mean absolute error View My Code: https://lnkd.in/gtPcqt8A Let's Connect: Share your thoughts on machine learning and data science! #MachineLearning #DataScience #FastAI #Kaggle #Titanic #RandomForest #DecisionTree #Python #AI #DeepLearning #DataAnalysis #DataVisualization #PredictiveModeling #SurvivalAnalysis #Classification #Regression Featured Skills: Machine Learning, Data Science, Python, FastAI, Kaggle, Data Preprocessing, Feature Engineering, Model Evaluation

🚀 Kicking off Week 2 of my Data Science with Python journey at #ArewaLadies4Tech! 🚀 ✅ Monday: Model Building Yesterday, we dove into the heart of data science: model building. This involved selecting the right algorithms and training them on our data to make accurate predictions. From linear regression to complex neural networks, it was all about finding the best fit for our data. It was exciting to see the different models we could build and how they performed! ⭕ Tuesday: Model Deployment Today, it's all about putting those models to work in the real world. Model deployment is the process of integrating a machine learning model into an existing production environment to make practical, data-driven decisions. This involves ensuring the model's performance in real time and maintaining its accuracy over time. It's the final step in bringing our hard work to life and making a tangible impact. #womenintech #ladiesinAI #Datascience #Dataanalytics