Niranjan Sendilkumar’s Post

Stop using Python without the right libraries. Raw Python slows you down. Libraries unlock real data science. NumPy for numerical computing. Pandas for cleaning and analyzing data. Matplotlib / Seaborn for visualization. Scikit-learn for machine learning. TensorFlow / PyTorch for deep learning. Tools don’t replace thinking. But the right stack makes thinking scalable. #Python #DataScience #MachineLearning #DeepLearning #PythonLibraries #NumPy #Pandas #ScikitLearn #TensorFlow #PyTorch #DataAnalytics #AI #LearnDataScience #TechSkills #InsightSeeker

🚀 Day 44/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 1: Decision Tree I began exploring classification algorithms in machine learning. Decision Trees help in making predictions by splitting data into branches based on conditions, making them easy to understand and interpret. Machine Learning is the core of modern AI systems, and I’m excited to continue learning more algorithms, models, and their real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

Recently completed a presentation on Jupyter Notebook for Machine Learning. In this, I covered: Basics and key features of Jupyter Notebook How it helps in building ML models step by step A simple Linear Regression example Data visualization using Python It is a powerful tool for learning, experimenting, and understanding machine learning concepts in a practical way. Looking forward to exploring more in Data Science and AI. #MachineLearning #DataScience #JupyterNotebook #Python #AI #Learning

Building in data science requires the right Python stack. Master NumPy and Pandas for data work, Matplotlib and Seaborn for visualization, Scikit-learn and boosting libraries for ML, and PyTorch or TensorFlow for deep learning. Tools matter, but fundamentals matter more. 📕 ebokify.com/python 📕 https://lnkd.in/d42rindX #Python #DataScience #MachineLearning #AI

𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝗿𝘁𝘀! The Darts library has simplified time series analysis and forecasting with Python. Darts supports various forecasting approaches, ranging from statistical models like ARIMA, to novel methods based on deep learning. Darts also supports advanced techniques like explainable forecasting, conformal prediction and anomaly detection. Therefore, Darts has been established as one of the best libraries for time series tasks, making it extremely useful to data scientists and researchers. Visit the link below for more information and follow me for regular data science content! 𝗗𝗮𝗿𝘁𝘀 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dEQepm3D 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #machinelearning #deeplearning

🚀 Day 45/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 2: Logistic Regression Today I explored Logistic Regression, one of the fundamental algorithms used for classification problems in machine learning. It helps predict the probability of an outcome, such as whether a patient has a disease based on medical data. Understanding these core algorithms is helping me build a strong foundation in machine learning and prepare for solving real-world problems using data. Machine Learning continues to be an exciting field, and I’m looking forward to exploring more algorithms and practical implementations in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #LogisticRegression #AIML #Python #LearningInPublic #DataScience

Why Python is the non-negotiable first step for AI. 🐍🤖 Body: If you're looking to transition into AI or Data Science, don't let the "code" part intimidate you. There’s a reason Python has become the industry standard: Low Barrier to Entry: It reads like English, making it the most beginner-friendly language out there. The "Power Tools": With libraries like NumPy, Pandas, and TensorFlow, you aren't building from scratch—you're standing on the shoulders of giants. Speed to Market: It’s built for fast development and automation, allowing you to go from an idea to a working model in record time. Closing: The AI revolution is being written in Python. Are you ready to start your first chapter? #Python #ArtificialIntelligence #DataScience #TechCareers #LearnToCode

📊 Projects are the fastest way to learn Machine Learning. If you're new to ML, start with simple projects like: ✔ Spam Email Detection ✔ Movie Recommendation System ✔ Image Classification ✔ House Price Prediction ✔ Chatbot These projects build strong foundations in Python, ML models, and data analysis. #MachineLearning #ArtificialIntelligence #DataAnalytics #Python #AI

Day 6/20 my AI/ML journey 🚀 One thing I’m starting to appreciate more is how much work happens before machine learning models are even involved. This week I spent time working on reading and exploring datasets using Python. Simple things like: Understanding the structure of the dataset Checking the data types of each column Looking for missing values Inspecting how different features are distributed At first it seemed basic, but the more I explore datasets the more I realize how important this stage is. If you don’t understand your dataset, you can’t build a reliable model. Data first. Models later. #africaagility #learninginpublic #AI #MachineLearning #DataScience #Python

My aim for the coming decade is clear: - Building a solid foundation in Data & AI I’m currently strengthening my knowledge in SQL and Python, focusing on how data can be structured, analyzed, and transformed into meaningful insights. My approach is simple: not just learning tools, but understanding the reasoning behind data, both in theory and in practice. What makes this journey particularly meaningful is the shift in perspective — seeing data not as simple numbers, but as a powerful tool for decision-making. #SQL #Python #AI #CareerTransition #DataAnalytics