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🚀 Project Spotlight: Email Spam Detection using Machine Learning and LSTM 📧🔍 Excited to share my latest project on email spam detection! With the rise of digital communication, identifying spam emails is crucial for maintaining secure and efficient inboxes. Here's a brief overview of what I accomplished: 🔹 Objective: Classify emails as spam or not spam using various machine learning models and LSTM networks. 🔹 Methods: - Data Preprocessing: Cleaned and transformed the email text data using NLTK (Natural Language Toolkit) and converted it into numerical features. - Machine Learning Models: Implemented Naive Bayes, SVM, and Random Forest for initial classification. - LSTM Networks: Leveraged the power of LSTM networks for improved classification performance. 🔹 Results: Evaluated models based on accuracy, precision, recall, and F1-score, showcasing the effectiveness of LSTM in handling text classification. 🔹 Technologies Used: Python, NLTK, Keras, TensorFlow, scikit-learn, Pandas. Check out the full project on my GitHub: https://lnkd.in/deWq7yuv This project highlights the intersection of traditional machine learning and deep learning, demonstrating the potential of advanced models in tackling real-world challenges. Looking forward to your thoughts and feedback! #MachineLearning #DeepLearning #DataScience #AI #SpamDetection #Python #NLTK #LSTM #ProjectShowcase

Build web application for Fruits Classification project to classify, whether a given fruit is fresh or rotten by giving an input image and getting output as fresh or rotten fruit using Deep Learning. Code link: https://lnkd.in/dQJwvUDT

🔥 Starting a 30-Day Interview Question Series! 🔥 Hey everyone, I'm thrilled to share that I'll be embarking on a transformative journey to master Artificial Intelligence and Machine Learning (AI/ML) over the next 30 days! This challenge is tailored specifically for me to deepen my understanding and expertise in this cutting-edge field. Here are 10 interview questions: 1) . What is the Central Limit Theorem and why is it important? 2) What is sampling? How many sampling methods do you know? 3)  What is the difference between type I vs type II error? 4) What do the terms p-value, coefficient, and r-squared value mean? What is the significance of each of these components? 5) What are the assumptions required for linear regression? 6) What is a statistical interaction? 7) What is selection bias? 8) What is an example of a data set with a non-Gaussian distribution? 9) What is the Binomial Probability Formula? 10)  What is bias-variance trade-off? Feel free to join me on this journey, share your knowledge, and cheer me on! Let's make the next 30 days a transformative experience in mastering AI/ML. #AI #MachineLearning #30DayChallenge #PersonalGrowth #DataScience #DeepLearning #Python

I built a deep learning multi-variate mulit-step forecast model autots or modeltime were what I had in mind. I was afraid deep learning forecasts would not give me valid results but they have as long as I have a small forecast horizon relative to the training data. 13 weeks to 3 years. I found modeltime and autots didn't explore multivariate deep learning and I figured since I've been using transformers applying pytorch to some keras time series code shouldn't be too hard. Here I'm just predicting on price, but the tool is able to handle more than one output prediction. I'm slowly but surely rebuilding my stock market framework, and this was the final cherry forecast at the very end that would tell me when to get out, before that I would do screener top k, significant regression filtering, markowitz profile. Repo: https://lnkd.in/gr4HCr2f #pytorch

🚀 Top 24 Machine Learning Interview Questions! Are you preparing for a machine learning interview or just want to test your knowledge? I have compiled a list of the top 24 questions which are mostly asked. Comment below with your answer to your favourite question, and I will send you a PDF with all the answers within 24 hours via direct message! 🌟 1. How do you handle missing data in a dataset? 2. Explain the bias-variance tradeoff. 3. What is overfitting, and how can you prevent it? 4. How do you evaluate the performance of a machine learning model? 5. Can you explain the concept of cross-validation? 6. How do you choose the right model for a given problem? 7. What is the purpose of regularization in machine learning? 8. What are ensemble methods and why are they used? 9. What is the ROC curve and why is it important? 10. Describe the backpropagation algorithm. 11. How do you handle imbalanced datasets? 12. What is clustering and what are its types? 13. Explain Linear Regression and Logistic Regression. 14. How does gradient descent work? 15. What are hyperparameters, and how do you tune them? 16. Describe the k-nearest neighbours algorithm. 17. What is the importance of the learning rate in training neural networks? 18. Explain the difference between bagging and boosting. 19. What is a Box-Cox transformation? 20. Can you mention some advantages and disadvantages of decision trees? 21. What are outliers? Mention three methods to deal with outliers. 22. What is the difference between regularization and normalisation?  23. What is Kernel in SVM? Last but not least... 24. List popular cross-validation techniques. Answer your favourite question below 😊. Let’s learn and grow together ✨. Share this post in your Network! Connect with me here - Shivani Khandelwal #datascience #machinelearning #interviewquestions #mlinterview

Lately, I’ve had several conversations with people who are eager to switch to a career in data science and machine learning but feel like the right moment has already passed. If that sounds familiar, I’m here to say: it’s definitely not too late. In fact, there’s never been a better time to jump in. I’m excited to share my first blog article, which explains why 2024 is a fantastic time to start learning machine learning. When I first considered getting into ML, it felt like a huge, complex world. But now, with AI transforming everything from healthcare to finance, the value of these skills is only growing. In the article, I suggest the learning path I would follow if I were to start again (one that builds a strong foundation before diving into more complex topics). Here’s a sneak peek into some points: • 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀: The fundamental tools you need to get started. • 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀: How basic models lay the groundwork for understanding the logic and mechanics behind more advanced models in deep learning. • 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆: What you need to know to ensure you can trust your model’s performance and avoid costly mistakes. • 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Why working with real-world data accelerates your learning and builds practical skills. • 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱 𝗡𝗟𝗣: When and why it’s worth diving into deep learning, CNNs, and large language models. I’ll leave the link to the article in the comments. I’d love to hear about your journey or your thoughts on where ML is headed! #MachineLearning #AI #CareerSwitch #DataScience #DeepLearning #NLP #Python #Innovation

ML Interview Question 78: 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐜𝐚𝐥𝐢𝐧𝐠? 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Data pre-processing is an integral step in building any machine learning algorithm, be it a traditional tree-based regression model or deep learning based image classification model. Here in this article we discuss about one of the most important data pre-processing methods: Feature Scaling and its various techniques. Key concepts covered in this article are as follows: ⭐ What is feature scaling? ⭐ Why is feature scaling done? ⭐ How to do feature scaling? ⭐ Which feature scaling method to choose when? ⭐ Common Machine Learning algorithms using feature scaling https://lnkd.in/ggza9Mye -- For a full list of Top 100 Machine Learning Questions with detailed answers, see this link: https://lnkd.in/gCyJ7DRb If you are preparing for Machine Learning interviews, please join us on: https://aiml.com #machinelearning #deeplearning #interviewquestionsandanswers

Hello Connections !! 👋 I am excited to share 📢 that I have completed the "Introduction to Deep Learning with Keras" course through Simplilearn. 🌟 Comprehensive program has been incredibly enriching, providing me with industry-relevant skills in deep learning. 🙌 The course delved deep into Keras, enhancing my practical knowledge and understanding of this powerful tool. It was a highly rewarding experience that has significantly boosted my proficiency in the field. #simplilearn #learning #deeplearning

🌟 Completion of My 90-Day Machine Learning Journey Hey everyone! 👋 🚀 After 90 non-stop days of learning, experimenting, and overcoming challenges, I’ve successfully completed my Machine Learning journey! 🌟 This was an incredible experience, filled with ups and downs, but I managed to stay focused and reach the finish line. Here’s a brief look at the topics I covered: 📊 Key Topics Covered: 🔹 Supervised Learning: Linear & Logistic Regression, Ridge, Lasso, ElasticNet, Decision Trees, Random Forest, Gradient Boosting, XGBoost 🔹 Unsupervised Learning: K-means, Hierarchical Clustering, DBSCAN 🔹 Ensemble Methods: Bagging, Boosting, AdaBoost, Stacking 🔹 Anomaly Detection: Isolation Forest, Local Outlier Factor, DBSCAN 🔹 Feature Engineering & Selection: PCA, Curse of Dimensionality, Encoding, Handling Outliers 🔹 Model Tuning: Hyperparameter Tuning with GridSearchCV & RandomizedSearchCV 🔹 Evaluation Metrics: ROC Curve, Confusion Matrix, Precision-Recall 🔹 Advanced Python Topics: Multithreading, Multiprocessing, Logging 🔹 Other Key Concepts: Naive Bayes, SVM, KNN, Feature Importance, Cross-validation 💡 Next Steps: I’ll take some time to apply these skills in real-world projects, gain practical experience, and then dive into Deep Learning and Natural Language Processing! 💻 I couldn’t be more excited about where this journey is heading, and I’m looking forward to continuing my exploration into more advanced topics. 🌱 Thanks to everyone who supported me along the way! 🌟 #MachineLearning #TechJourney #90DaysOfML #DeepLearningNext #NLPJourney

Hello Everyone, "Diving deep into the world of Machine Learning! 🤖📚 I'm Sharing my notes to solidify my understanding and help others on their ML journey". Since we'll go from basics, I'm open to suggestions and topics that we should cover in future. Let's learn together! 🚀 Summary Gradient Descent is an optimization algorithm widely used in machine learning and deep learning. It iteratively adjusts parameters (weights) to minimize a cost function, which measures how far predictions are from actual values. Gradient Descent drives the model to its optimal state by finding the minimum point of the cost function. Highlights 🔍 Purpose: Gradient Descent minimizes the cost function by adjusting parameters in small steps to make predictions as close to actual values as possible. ⚙️ Cost Function: The cost function (or loss function) measures the error in predictions. Common examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. 📈 Learning Rate: The step size taken during each iteration is controlled by the learning rate. A high learning rate may overshoot the minimum, while a low rate can make convergence slow. Key Insights 🎯 Gradient: The gradient is the slope of the cost function at a given point. It indicates the direction and magnitude of changes to make in the weights to reduce error. 🔁 Iterative Process: Gradient Descent updates the parameters iteratively, gradually moving closer to the minimum cost. ⚖️ Types: Batch Gradient Descent: Uses the entire dataset to calculate gradients, making it stable but slow for large datasets. Stochastic Gradient Descent (SGD): Uses one data point at a time, introducing more variability but improving speed. Mini-batch Gradient Descent: Balances the two, using small batches of data for each iteration. #MachineLearning #GradientDescent #CostFunction #Optimization #DataScience #PythonCode #MLAlgorithms #AI #LearningJourney #LinkedInLearning