https://lnkd.in/esa_jyk2 Sponsor by BUILDUP-AI.COM.BR Intermediary Python with real AI/ML - Learning Roadmap By Rubem Didini Filho. Outline; 1. Introduction section with why learn Python/AI 2. Beginner's Roadmap with phases 3. Course Comparison (now in plain text instead of table) 4. Getting Started Checklist 5. OOP Practice Guide 6. Community Advice 7. Next Steps & Resources 8. FAQ 9.APPENDIX 1: Welcome to your journey into Python and AI programming! Python is the 1 language for AI, machine learning, and data science. It's beginner-friendly, in high demand across industries, and incredibly versatile. Whether you want to build AI models, automate tasks, or create web applications, Python gives you the tools to make it happen. AI is transforming how we live and work, and learning to use Python for AI will open doors to exciting careers and creative projects...

https://lnkd.in/eHjVY9tJ Intermediary Python with real AI/ML - Learning Roadmap By Rubem Didini Filho. Outline; 1. Introduction section with why learn Python/AI 2. Beginner's Roadmap with phases 3. Course Comparison (now in plain text instead of table) 4. Getting Started Checklist 5. OOP Practice Guide 6. Community Advice 7. Next Steps & Resources 8. FAQ 9.APPENDIX 1: Welcome to your journey into Python and AI programming! Python is the 1 language for AI, machine learning, and data science. It's beginner-friendly, in high demand across industries, and incredibly versatile. Whether you want to build AI models, automate tasks, or create web applications, Python gives you the tools to make it happen. AI is transforming how we live and work, and learning to use Python for AI will open doors to exciting careers and creative projects.... Sponsor by BUILDUP-AI.COM.BR

🚀 Day 1 of My Artificial Intelligence Learning Journey Today I started strengthening my Python fundamentals, which are essential for learning Artificial Intelligence and Machine Learning. Here are some concepts I learned today: 🔹 Python Variables – used to store and manipulate data 🔹 Variable Naming Rules – proper naming conventions in Python 🔹 Python Data Types – int, float, string, list, tuple, dictionary, set, boolean 🔹 Strings in Python – text data using single or double quotes 🔹 Variable Scope – local vs global variables 🔹 Python Operators – arithmetic, assignment, comparison, logical, membership, and bitwise operators 📌 Key Takeaway: A strong understanding of Python fundamentals is important before diving deeper into AI and Machine Learning. This is Day 1, and I’m excited to continue learning and sharing my journey. #Python #ArtificialIntelligence #MachineLearning #AIJourney #LearningInPublic

https://lnkd.in/esa_jyk2 Intermediary Python with real AI/ML - Learning Roadmap By Rubem Didini Filho. Outline; 1. Introduction section with why learn Python/AI 2. Beginner's Roadmap with phases 3. Course Comparison (now in plain text instead of table) 4. Getting Started Checklist 5. OOP Practice Guide 6. Community Advice 7. Next Steps & Resources 8. FAQ 9.APPENDIX 1: Welcome to your journey into Python and AI programming! Python is the 1 language for AI, machine learning, and data science. It's beginner-friendly, in high demand across industries, and incredibly versatile. Whether you want to build AI models, automate tasks, or create web applications, Python gives you the tools to make it happen. AI is transforming how we live and work, and learning to use Python for AI will open doors to exciting careers and creative projects. This guide introduces you to five top-tier courses, outlines a clear learning path, and shares practical advice from the global developer community. By the end, you'll know how to choose the right course, what to expect, and how to build a portfolio of AI-powered projects. Why Python for AI? Python is the main language for machine learning and AI development. It's readable, has a massive ecosystem of libraries, and is supported by a vibrant community. .. Sponsor by BUILDUP-AI.COM.BR

🚀 Day 2 of My Artificial Intelligence Learning Journey Continuing my Python learning journey for AI and Machine Learning, today I explored some important data structures and concepts in Python. Here’s what I learned today: 🔹 Stacks and Queues – Understanding how data can be organized and processed using LIFO (Stack) and FIFO (Queue). 🔹 Queue Implementation – Practiced using Python’s queue module and collections.deque. 🔹 Lists – Learned how lists store collections of items and explored common methods like append(), insert(), remove(), and pop(). 🔹 Dictionaries – Key-value data structure used to store and access data efficiently. 🔹 Sets – Unordered collection of unique elements and useful methods like add(), remove(), and discard(). 📌 Key Takeaway: Understanding data structures in Python is essential because they help organize and process data efficiently—an important skill for building AI and machine learning models. Excited to continue learning and building a strong foundation in Python for AI. #Python #ArtificialIntelligence #MachineLearning #DataStructures #LearningInPublic #AIJourney

I’ve been building a machine learning–based approach to extract data from engineering graphs. 📊 The goal is to take graph images (like pressure vs depth) and convert them into structured, usable data instead of relying on manual digitization. I developed a Python pipeline using OpenCV and explored ML-based approaches to improve how curves are detected and separated — including experimenting with U-Net for segmentation and a CNN-based model for prediction.🤖🧠 One of the more challenging parts was getting consistent curve detection and accurately mapping pixel values to real-world units. It took quite a bit of iteration to get the extracted output to closely match the original graph behavior. On the left is the Original Graph, and on the right is the extracted output. I’m really happy with how it’s coming together so far, especially working on something that connects machine learning with a practical, real-world use case.🚀 Tools used: Python, OpenCV, NumPy, Pandas, CNN, U-Net 💻 Sharing a snapshot of the output below 👇 #MachineLearning #DataAnalytics #ComputerVision #Python

🚀 Python Ecosystem for Data & AI From data analysis to machine learning and generative AI, the Python ecosystem provides powerful libraries that make complex problems easier to solve. 📊 Data Science: NumPy, Pandas, SciPy, Matplotlib, Seaborn, Plotly 🤖 Machine Learning: Scikit-Learn, TensorFlow, PyTorch, XGBoost, LightGBM ✨ Generative AI: JAX, StyleGAN, NeRF, DALL·E, Imagen Mastering these tools opens the door to building data-driven solutions, intelligent systems, and next-generation AI applications. Python continues to be the backbone of modern Data Intelligence and AI innovation. 💡 Which Python library do you use the most in your projects? #python #Datascience #machinelearning #Artificialintelligence #programming

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 03 ). ✓ Statement:- 1) ----- Write a program that ask the user for a single number. 2) ----- The program should tell the user if that number is even or odd. 3) ----- Hint: Use the % ( reminder ) operator. 4) ----- Hint: If a number is divided by 2 leaves 0 reminder it is even. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

Why is Python the most widely used language for AI and Machine Learning instead of Java? 🤔 Python has become the foundation of modern AI development because of its simplicity, powerful ecosystem, and flexibility. Here are some key reasons: ✔ Simple and readable syntax – faster learning and development ✔ Powerful AI/ML libraries – TensorFlow, PyTorch, Scikit-learn ✔ Rapid prototyping – easy to experiment with models ✔ Strong open-source community – continuous innovation ✔ Integration with high-performance languages like C/C++ Because of these advantages, Python has become the leading language for AI, Machine Learning, and Data Science. If you're starting your journey in AI/ML, Python is one of the best languages to begin with. #Python #MachineLearning #ArtificialIntelligence #DataScience #AI #Programming #TechLearning #AIEngineering #DataAnalytics #FutureOfAI

Day 4 of learning Python in public 🚀 Today I focused on understanding Python Lists and how Python works with collections of data. Key things I learned: • Creating lists and storing multiple values in a single structure • Accessing elements using indexing and negative indexing • Using slicing to retrieve specific ranges of elements • Adding items using append(), insert(), and extend() • Removing items using remove() and pop() • Updating list elements using indexing • Checking if an element exists in a list using the in operator • Sorting lists using sort() and sort(reverse=True) • Important list methods like count(), index(), copy(), and clear() • Working with nested lists and understanding matrix[row][column] access • Using enumerate() to get both index and value while looping • Using zip() to combine multiple lists together • Writing concise transformations using list comprehension Big takeaway: Lists are one of the most fundamental data structures in Python. Understanding how they work makes data manipulation much easier and builds a strong foundation for more advanced concepts. Continuing to strengthen the fundamentals step by step. #Python #DataScience #LearningInPublic #Programming #DataScienceJourney #softwareengineering #AI #MachineLearning