Mohammad Moin’s Post

🚀 Exciting Journey Alert! 🌟 Recently, I embarked on an exhilarating learning adventure exploring the depths of Azure Machine Learning. The process of building pipelines and mastering hyperparameter tuning has been nothing short of thrilling! 💻 Azure's robust platform provided an immersive experience, empowering me to optimize models and fine-tune parameters for maximum performance. With every step, I delved deeper into the intricacies of machine learning, eager to unlock new possibilities. But the excitement doesn't end there! Alongside Azure Machine Learning, I also delved into the captivating realm of generative artificial intelligence models for Natural Language Processing (NLP). 📊 Discovering the potential of these models to understand and generate human-like language has been truly awe-inspiring. From text generation to sentiment analysis, the applications of NLP are boundless, and I'm excited to explore them further. The fusion of Azure Machine Learning and NLP has ignited a spark of creativity within me. 🧠💬 I can't wait to leverage these cutting-edge technologies to tackle real-world challenges and drive innovation. The journey ahead is filled with endless possibilities, and I'm thrilled to be at the forefront of this exciting evolution in AI and data science! 🚀💼 #AzureMachineLearning #HyperparameterTuning #NLP #GenerativeAI #DataScience #AI #MachineLearning #Azure #ExcitingJourney #KedrusAcademy#ContinuousLearning 🌟🔍

🚀 Embarking on an Exciting Journey in AI and Software Development! 🚀 Today marks the second milestone in my journey into the captivating realm of Artificial Intelligence, Machine Learning, and Deep Learning. 🌟 After delving into the fundamentals, I've found myself immersed in the intricate world of Natural Language Processing (NLP), a cornerstone of countless real-world applications. From powering auto-suggestions and language translations to crafting sophisticated chatbots and condensing content, NLP stands as a transformative force in our interaction with technology. 💬🌐 For my latest project, I've delved into training a basic NLP model using over 1.6 million tweets sourced from a Kaggle dataset. This endeavor not only solidified my grasp of AI concepts but also highlighted the practical impact these technologies can have in tackling everyday challenges. Check out my project on GitHub: https://lnkd.in/dg24MfX2 I'm thrilled to continue honing my skills in AI while leveraging my background in software development. The road ahead is promising, and I'm eager to witness the evolution of my journey. #AI #MachineLearning #DeepLearning #NLP #SoftwareDevelopment #TechInnovation #LearningJourney #ArtificialIntelligence #DataScience #GitHub #ProjectShowcase Stay tuned for more updates and exhilarating projects! 🚀🔍

🚀 Exciting Open Source Project Alert! 🚀 Are you passionate about AI, Machine Learning, Deep Learning, Computer Vision, and NLP? Look no further! 🌟 I’ve recently stumbled upon an incredible GitHub repository that’s a treasure trove of 500 projects in these cutting-edge fields. 🤯 Whether you’re a seasoned developer or just starting out, there’s something here for everyone. 🔗 Repository Link: 500 AI/ML/DL/CV/NLP Projects: https://bit.ly/4cXZ8Wf 🌟 Why You Should Check It Out: Diverse Projects: From image recognition to natural language processing, this repo covers it all. Explore fascinating use cases and learn from real-world examples. Well-Documented: Each project comes with detailed explanations, code snippets, and resources. It’s like having a personal mentor guiding you through the process. Community Engagement: Join a vibrant community of developers, share your insights, and collaborate on exciting projects. 🤝 Remember, knowledge grows when shared. Let’s empower each other and make a difference! 🌐💡 . . . #AI #MachineLearning #DeepLearning #ComputerVision #NLP #OpenSource #GitHubProjects

🚀 Excited to share the completion of an impactful project on Fraud Detection! 🚀 Over the past few months, I've been diving deep into the realms of data extraction and deep learning, with a special focus on Natural Language Processing (NLP). This journey has been both challenging and rewarding, and I'm thrilled to see the results. 🔍 Key Highlights of the Project: Data Extraction: Efficiently gathered and processed vast amounts of data from multiple sources to ensure comprehensive analysis. Deep Learning: Utilized state-of-the-art deep learning techniques to build a robust model capable of identifying fraudulent activities with high accuracy. NLP: Leveraged NLP to analyze textual data, uncovering hidden patterns and anomalies that are indicative of fraud. This project not only enhanced my technical skills but also underscored the importance of advanced technologies in combating fraud. The insights gained will undoubtedly contribute to more secure and trustworthy systems. A big thank you to my team and mentors for their support and guidance throughout this journey. I'm looking forward to applying these skills in future projects and continuing to push the boundaries of what's possible with AI and machine learning. Follow this link to my GitHub Repository - https://lnkd.in/gy4fZgwk #FraudDetection #DeepLearning #NLP #DataScience #MachineLearning #AI #DataExtraction #TechInnovation

REGRESSION 📈: It is a supervised learning algorithm that predicts a continuous value, such as the price of a stock or the number of sales. CLASSIFICATION 📊: It is a supervised learning algorithm that predicts a categorical value, such as whether an email is spam or not. NATURAL LEARNING AND DEEP LEARNING 🧠: They are a type of supervised learning algorithm that uses artificial neural networks to learn from data. Deep learning algorithms are often used for tasks such as image classification, natural language processing, and speech recognition. TRANSFER LEARNING 🔄: It is a technique that uses a model trained on one task to improve the performance of a model trained on a different task. CLUSTERING 📂: It is an unsupervised learning algorithm that groups similar data points together. REINFORCEMENT LEARNING 🎮: It is a type of unsupervised learning algorithm that learns by trial and error. DIMENSIONALITY REDUCTION 📉: It is a technique that reduces the number of features in a dataset without losing too much information. NLP 🗣️: It is a field of computer science that deals with the interaction between computers and human (natural) languages. ENSEMBLE METHODS 🤝: It combine multiple machine learning models to improve the accuracy of the predictions. COMPUTER VISION📷📹: It is a field of computer science that deals with the extraction of meaningful information from digital images or videos. Follow, like, and share! Check our courses and book your free consultation call - 01203113765. #machinelearningtools #datascience #deeplearning #dailygoals #datascientist #machinelearning #python #artificialintelligence #bigdata #dataviztools #datasciencejobs #dataanalytics #regression #classification #clusteringdata #dimensionality #ensemble #nlp #neutralnetworks #digicrome #india #innovation

: 🌟 Exciting Achievement! 🌟 I'm thrilled to share that I have completed an extensive course on Machine Learning! This learning journey has been transformative, equipping me with cutting-edge skills and hands-on experience in various facets of machine learning. Here’s a glimpse of what I’ve learned and accomplished: 🔍 Supervised Learning: Mastered various Regression Algorithms including Linear Regression, Polynomial Regression, etc. Delved into multiple Classification Algorithms such as Logistic Regression, Decision Tree Classification, Random Forest Classifier. 🔍 Unsupervised Learning: Explored numerous Clustering Algorithms such as K-Means, Hierarchical Clustering, etc. 💼 Projects: Developed projects utilizing Supervised and Unsupervised ML techniques Executed projects in Computer Vision and Natural Language Processing (NLP) 🛠️ End-to-End ML Lifecycle: Applied the entire machine learning lifecycle to my projects, from data preprocessing and model building to evaluation and deployment A huge thank you to [Course Provider/Instructor] for the excellent curriculum and support throughout this course. I’m excited to leverage these new skills to solve real-world problems and innovate in the field of machine learning. #MachineLearning #SupervisedLearning #UnsupervisedLearning #RegressionAlgorithms #ClassificationAlgorithms #ClusteringAlgorithms #ComputerVision #NLP #DataScience #ArtificialIntelligence #ContinuousLearning #CareerGrowth

Hi #Connections 🚀 Excited to Share My Latest NLP Project! 🚀 I am thrilled to announce the completion of my recent Natural Language Processing (NLP) project, where I delved deep into sentiment analysis using various machine learning models. This project was an incredible journey of exploring and implementing different techniques to classify sentiments in social media reviews. Here’s a brief overview of what I accomplished: 🔍 Data Preprocessing: Loaded and explored the dataset, identifying and handling missing values. Visualized the distribution of sentiments using Seaborn to understand the data better. Removed irrelevant data to focus on relevant sentiment analysis. 💡 Text Processing: Tokenized tweets using NLTK's TweetTokenizer. Cleaned text by removing special characters and filtering out tokens with a length less than 3. Applied stemming using SnowballStemmer and removed stopwords to streamline the text. 📊 Feature Engineering: Utilized TF-IDF Vectorization to transform text data into numerical format suitable for machine learning models. 🧠 Model Building & Evaluation: Trained several classifiers: K-Nearest Neighbors, Support Vector Machine, Multinomial Naive Bayes, Decision Tree, and Random Forest. Evaluated each model using confusion matrices and accuracy scores to identify the best-performing algorithm. 🔧 Hyperparameter Tuning: Performed GridSearchCV on the K-Nearest Neighbors classifier to optimize hyperparameters, achieving the best model performance. 🔗 GitHub Repository: https://lnkd.in/gPC-pGr7 🙏 I'm grateful for the mentorship from Sabir K K at Luminar Technolab Technolab and excited to continue exploring the fascinating field of machine learning! Let's connect and discuss more about data science and AI. #NLP #MachineLearning #DataScience #AI #TechInnovation

When I began my journey into AI and ML, I knew it would be a long and challenging path (my inner voice : the most challenging parts are still ahead). So lately, I decided to test my skills through certification, and I am proud to say that I am now a ```Certified TensorFlow Developer``` through the TensorFlow Certificate Program; yes from the makers itself. Now just to give an overview of my recent AI journey; Over the past few months, my focus has been on projects utilizing state-of-the-art (SOTA) models. This includes transformers for image analytics and sequence models for natural language processing (NLP). Recently, I've been delving into the complexities of handling pre-trained large language models (LLMs). This journey has been the most exciting and thrilling experience I’ve had so far. It has even reached a point where I sometimes find myself thinking and speaking in terms of machine learning concepts during normal conversations. I am eager to continue exploring new frontiers in AI and ML, fine-tuning my skills and knowledge along the way. Thank you to everyone who has supported me on this incredible journey. NB: This post was developed from scratch by me and fine tuned by AI :) TensorFlow | Google #AI #MachineLearning #TensorFlow #Transformers #NLP #LLM #ArtificialIntelligence #DeepLearning

🚀 Excited to share my latest project on LinkedIn! 🚀 I've just completed a sentiment analysis model on a movie dataset, and I'm thrilled with the results! 🎬📊 Github repository :https://lnkd.in/du2kzVwP Key Highlights: ✨ Data Cleaning: Removed HTML tags, stop words, and performed stemming. ✨ Model Accuracy: Achieved an impressive ~83% accuracy. This project was a fantastic journey through the nuances of natural language processing and machine learning. Grateful for all the resources and support that helped make this possible! #MachineLearning #DataScience #NLP #SentimentAnalysis #DataCleaning #AI #MovieData

🌐 Project Name. : NLP Twitter Sentiment Analysis ⚙️ Machine Learning models : Knn , Naive Bayes , SVC 🖥️ Platform. : Kaggle 💻 🧑🏻🏫 Guide : Sabir K 1. ♻️ Data Preprocessing: Transforming raw data into insights! - Change data types: Ensure uniform formats for seamless analysis. - Drop columns: Remove unwanted features for efficient modelling. 2. 📊 Data Visualisation : - Use Seaborn to Visualize data patterns effectively. 3.🗣️ NLP(Natural Language Processing) - Tokenization: Splitting text into individual words, phrases, or sentences. - Removing Special Characters - Stopword Removal: Filtering out common words that do not carry significant meaning. - Stemming: Reducing words to their root form to normalize variations (e.g., "running" to "run") - TF-IDF Vectorisation : Term Frequency-Inverse Document Frequency (TF-IDF) transform text into feature vectors, allowing algorithms to process and analyze textual data effectively. 4. ⚙️ Model Creation: Turning data into predictions! - Machine learning model: Develop models for Prediction - Use powerful ML algorithms: Employ algorithms like Knn , Naive Bayes , SVC for accurate predictions. 5. ⚖️ Model Evaluation: Assess the performance of the sentiment analysis model using evaluation metrics like accuracy, precision, recall, and F1-score. #DataPreprocessing #DataVisualization #NLP #ModelCreation #ModelEvaluation #SentimentAnalysis"