Boost ML skills with Sagar Chouksey's Python & PyTorch tutorials

Most people jump straight into GenAI tools without the foundations. They guess at prompts. They can't debug outputs. They can't improve anything. The **AI Fundamentals** Bundle changes that. 4 courses. Built in sequence. Designed to make you a contributor — not just a user — of GenAI applications. 🐍 Course 1 — Python Essentials for Data Science / ML The operating language of AI. Core Python, NumPy, Pandas, visualization, and an intro to scikit-learn and deep learning libraries. #AIFundamentals #GenAI #MachineLearning #DataScience #Python #LearningAndDevelopment #Upskilling #Grokkers

Understanding the difference between Independent and Dependent variables is one of the most important basics in Machine Learning. If you don’t understand this well, many ML concepts will feel confusing. In simple terms: X → Inputs (Features) Y → Output (Target) I explained it step by step with clear examples Save this post for later and follow for more AI & Python content #MachineLearning #AI #Python #DataScience #LearnAI

Source Code : https://lnkd.in/dtMUiv6W Title: Real-Time Handwritten Digit Classifier (MNIST) Tech Stack: Python, Scikit-Learn (MLP), Streamlit, NumPy, PIL Description: Developed a full-stack Machine Learning application that recognizes handwritten digits (0-9) with high precision using a Multi-Layer Perceptron (MLP) neural network. This project highlights the transition from a standard dataset (MNIST) to a functional, real-world web application. #ArtificialIntelligence #MachineLearning #DeepLearning #Python #Streamlit #COMSATS #MLEngineering #DigitRecognition link

🚀 Learning Update: Python (Week Progress) Continuing my Python journey as part of my path toward AI, Machine Learning, and Data Science. This week, I focused on understanding some important concepts: • Lambda Functions • Nested Functions • Class Methods (like str, len) • Basics of Polymorphism (Function Overloading concept) --- 💡 What made the difference this time: Instead of just learning theory, I focused on small practical implementations. For example: → Using lambda for quick one-line operations → Understanding how nested functions control scope → Customizing class behavior using built-in methods → Exploring how polymorphism changes function behavior --- 🧠 The key realization: Concepts make more sense when applied — even in small examples. --- 🔥 Step by step, building the foundation. More practical learning updates coming soon. --- 💬 What concept helped you understand Python better? comment ✍️ #Python #LearningJourney #AI #MachineLearning #DataScience #Programming #BuildInPublic #DeveloperJourney #TechLearning #Consistency

Day 3 of my AIML journey 🚀 Started with Python basics… Now stepping into Machine Learning 🤖 Today I learned: → What is Machine Learning → Types of ML (Supervised vs Unsupervised) Still a bit confused 😅 Trying to understand it step by step Anyone learning AI like me? 👇 #AI #MachineLearning #Python #AIML #100DaysOfCode #LearningInPublic

If you want to learn about Python, Computer Vision and ML applications, I highly recommend the series of videos from @digitalsreeni . He teaches from basic Python, non-ML algorithms to application of Neural Networks on processing images. Everything from a science perspective but with great applications on industry. Go and check it out, it is totally worth it. https://lnkd.in/es3Qz_D8

Starting my journey in Machine Learning! Today, I worked on a simple Linear Regression model using Python and Scikit-learn. 🔹 Created a dataset with input (X) and output (y) 🔹 Trained the model using Linear Regression 🔹 Predicted the output for a new input value This small step helped me understand how machines can learn patterns from data and make predictions. Key takeaway: Even a simple model can give powerful insights when the relationship between data is clear. Looking forward to exploring more concepts like classification, model evaluation, and real-world datasets! #MachineLearning #Python #DataScience #LearningJourney #AI #StudentLife

Discover the top 5 Python libraries for AI and machine learning, including TensorFlow, PyTorch, Scikit-learn, Keras, and OpenCV, and learn how to choose the best library for your project https://lnkd.in/gH7hN3M6 #PythonLibrariesForAi Read the full article https://lnkd.in/gH7hN3M6

The **AI Fundamentals** Bundle  🤖 Course 4 — Machine Learning: Primer Module GenAI is built on ML foundations. Clustering, classification, regression, and ML workflows including MLOps basics. #AIFundamentals #GenAI #MachineLearning #DataScience #Python #LearningAndDevelopment #Upskilling #Grokkers

💡 Prompt Engineering Challenge – Can you pick the best one? Not all prompts are created equal. The way you ask matters just as much as what you ask. 🧠 Drop your answer in the comments — which option is the MOST effective? --- 🚀 Learning side by side: 🐍 Python + 🤖 Prompt Engineering = Powerful combo for the future ✔ Be clear and specific ✔ Give context and constraints ✔ Ask for structured output ✨ Quick insight: Better prompts → Better answers Clarity + context = powerful results Small improvements in prompts can level up your AI skills --- Keep learning. Keep experimenting. Keep improving. #PromptEngineering #Python #AI #Learning #TechSkills #FutureOfWork #AItools #Upskill