Aravinth B’s Post

The code is on Spam sms detection by using the NLP and Machine learning model. In this code i have build model that can classify SMS messages as spam or legitimate. Use techniques like TF-IDF or word embeddings with classifiers like Naive Bayes, Logistic Regression, or Support Vector Machines to identify spam messages. #Encryptix #internship #datascience

I'm excited to announce the successful completion of my Data Science INTERNSHIP at Arjun Vision Tech Solutions! 🎓 During this program, I have: ✨ Developed a solid foundation in AI, ML, DL, and Data Science, understanding their interconnections and applications. 🤖 ✨ Acquired proficiency in data collection, preprocessing, and manipulation using tools like NumPy and Pandas, and conducted exploratory data analysis to derive actionable insights. 📊 ✨ Applied machine learning models, including regression and clustering algorithms, and deepened my understanding of neural networks and optimization techniques like gradient descent. 📈 ✨ Gained hands-on experience with NLP techniques using libraries like NLTK and SpaCy, focusing on tasks such as tokenization, stemming, lemmatization, and named entity recognition. 💬 Successfully completed a comprehensive data science project, demonstrating my ability to apply theoretical knowledge to real-world scenarios and effectively communicate my findings. 📝 I’m grateful to the instructors and colleagues for their guidance and support. Looking forward to applying these skills in my professional journey! 🚀 #DataScience #AI #ML #NLP #ProfessionalDevelopment #CareerGrowth #ArjunVisionTechSolutions

🔍 New Project Alert! 🖥️ I’ve just completed my second task for my data science internship at Brainwave Matrix Solutions! 🎉 This time, I worked on sentiment analysis using a Lexicon-based approach to analyze Amazon reviews. Here’s a quick overview: - 🛠️ Project: Analyzed product reviews to uncover customer sentiment. - 📊 Approach: Lexicon-based sentiment analysis, where I used predefined dictionaries of positive and negative words to classify reviews. -✍️ Text Preprocessing: Implemented natural language preprocessing techniques, including tokenization, stop-word removal, and stemming/lemmatization to clean and prepare the text data. - 📈 Results: The analysis showed that 80% of the reviews had positive sentiment, 12.6% were negative, and 7.4% were neutral. I’m looking forward to any feedback or suggestions from the community! 😊 #DataScience #SentimentAnalysis #AmazonReviews #NLP #LexiconBasedAnalysis #InternshipJourney #BrainwaveMatrixSolution Github Link : https://lnkd.in/g3YeBDzb

Second task at CodeAlpha ‘s ML internship. This project focuses on Twitter sentiment analysis using machine learning and NLP techniques. It involves preprocessing tweets, converting text data into numerical form using TF-IDF Vectorizer and training Logistic Regression to classify tweets as positive or negative. The trained model is saved for predictions. Here’s a github repository link: https://lnkd.in/d_a5SVVi

🚨𝗙𝗮𝗸𝗲 𝗡𝗲𝘄𝘀 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🚨 As part of my 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 at CodexCue Software Solutions, I developed a 𝗥𝗲𝗮𝗹 𝘃𝘀. 𝗙𝗮𝗸𝗲 𝗡𝗲𝘄𝘀 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 to identify credible and misleading news articles. 📰 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: ► I used text preprocessing techniques like stemming and stopword removal. ► Applied TF-IDF for text vectorization and Logistic Regression for classification. ► The model achieved 𝟵𝟱.𝟰𝟯% 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 on the Kaggle dataset. 🎯 I'm excited to apply these techniques to real-world challenges. 🔗 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯:https://lnkd.in/dMnyWtBu #MachineLearning #DeepLearning #TextClassification #FakeNews #AI #DataScience #NLP #Internship #Codex

||Day32||🎇 🚀 I am excited to kick off my summer internship in Data Science at Regex Company! 🌟 today we learn about🤷♀️🤷♀️️ 🔍 Mastering Gradient Descent: The Key to Efficient Learning in Machine Learning! 🔍 Gradient Descent is one of the most powerful optimization algorithms used to minimize errors in machine learning models. Whether you're working with linear regression, neural networks, or deep learning, this algorithm helps find the best possible parameters by iteratively adjusting model weights. 💡 Key Takeaways: • Optimization: Finds the minimum error by adjusting weights step-by-step. • Learning Rate: The pace at which your model learns—too fast or too slow, and you might miss the optimal point. • Types: From Batch to Stochastic and Mini-batch Gradient Descent, each has its unique advantages. #MachineLearning #DataScience #AI #GradientDescent #Optimization #ArtificialIntelligence #DeepLearning REGex Software Services

🌟 Exciting Update! 🌟 I’m excited to share that I’ve completed my AI internship at Code Alpha! 🎉 Key projects: Developed real-world ML models Applied NLP techniques to innovative solutions Had a great experience and looking forward to share more updates soon #ArtificialIntelligence #CodeAlpha

🌟 Exciting News! 🌟 I am thrilled to share that I have successfully completed three challenging and rewarding internship tasks through ShadowFox! 🚀 1️⃣ Image Classification with TensorFlow: Task: Built a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. Tools Used: TensorFlow and CNN. Achievements: Enhanced my skills in deep learning, model training, and image preprocessing. 2️⃣ Car Selling Price Prediction and Analysis: Task: Developed a machine learning model to predict car selling prices and performed an extensive analysis of the factors influencing these prices. Tools Used: Scikit-learn, Pandas, and Matplotlib. Achievements: Improved my understanding of regression techniques, feature engineering, and data visualization. 3️⃣ Implementing and Analyzing a Large Language Model (LLM): Task: Implemented a Retrieval-Augmented Generation (RAG) system and conducted an in-depth analysis of its performance and capabilities. Tools Used: Llama2 model from Hugging Face, Pinecone, Python. Achievements: Gained insights into natural language processing (NLP), RAG architecture, and the practical applications of LLMs. A heartfelt thank you to the entire ShadowFox team for their incredible support and guidance throughout these tasks. Your mentorship and feedback have been invaluable, and I am grateful for the opportunity to learn and grow through this experience. You can check the project code for these tasks through my GitHub repository from the link below. https://lnkd.in/dDKxBeiq Looking forward to applying these skills and knowledge in future projects and continuing my journey in the field of machine learning and artificial intelligence! 🤖✨ #MachineLearning #DeepLearning #DataScience #NLP #ArtificialIntelligence #TensorFlow #ScikitLearn #HuggingFace #Internship #ShadowFox #CareerGrowth

Hey everyone! 🌟 I'm thrilled to share that As a part of #Internship with Innomatics Research Labs. I've successfully integrated two cool tools, MLflow and Prefect, to make my Sentiment Analysis project even better. 😄🎉 #MLflow is an open-source platform for machine learning that provides end-to-end tracking, documentation, packaging, and Schedule deployments of machine learning workflows.🔓 Now, I can easily keep track of my experiments and manage my models effectively. With MLflow, I also created neat charts which shows model performance and hyperparameters tuning.📈 Big thanks to Innomatics Research Labs and Kanav Bansal for giving me the chance to dive into this awesome world of MLOps! 🙌 Check out the #MLops video I've provided to see a dynamic demonstration of my experiment tracking and model management in action! 🎬 please go through my Github link: https://lnkd.in/gcmiZ9nB #Fullstack_Data_Science #Machine_Learning #MLops #Orchestration #NLP #Hyperparameter_Tuning

🚀 Internship Achievement: Developing an SMS Spam Classifier with Machine Learning! 🚀 I'm excited to share a key milestone from my recent internship at CodSoft, where I built a robust model to classify SMS messages as either spam or legitimate. 📲🛡️ 🔍 Project Highlights: Using advanced natural language processing techniques, I developed and fine-tuned models to effectively identify spam messages and ensure the legitimacy of SMS communications. 🛠️ Tech Stack & Algorithms: Feature Extraction: Leveraged TF-IDF and word embeddings to convert text into powerful features. Classifiers Utilized: Naive Bayes Logistic Regression Support Vector Machine (SVM) Performance Metrics: Evaluated models using accuracy, precision, recall, and F1-score to determine their effectiveness. 💡 Key Learnings: Naive Bayes: Quick and efficient, providing a strong baseline for text classification. Logistic Regression: Enhanced accuracy with easily interpretable outcomes. SVM: Excelled in high-dimensional spaces, delivering superior classification results. 📊 Project Impact: Accurately identifying spam can significantly enhance user experience by minimizing unwanted messages and improving security. 📁 Dive into the Details: Explore the full project on GitHub, including the dataset, code, and comprehensive analysis: https://lnkd.in/dKSGUN2v 👥 Join the Conversation: Let's connect! I'm looking forward to engaging with others interested in NLP and machine learning to share insights and discuss innovative solutions. #MachineLearning #NLP #DataScience #SpamDetection #TextMining #InternshipProjects #Codsoft #Python #AI #BigData