A3 IT SKILLS SOLUTIONS’ Post

Most AI tutorials overwhelm beginners with theory. This one is different. In this hands-on AI tutorial using Python, you: • Set up a proper AI development environment • Build a spam classifier with scikit-learn • Train a CNN image classifier using TensorFlow • Create a sentiment analysis pipeline • Learn how to debug real-world AI errors It’s designed for developers who want working AI projects, not just concepts. If you're starting your AI journey in 2026, this is a practical roadmap. https://lnkd.in/dauJPakr #ArtificialIntelligence #MachineLearning #Python #DeepLearning #AIProjects #DataScience #TechCareers

Day 25 of learning Generative AI 🤖 Today’s focus: 📌 Area: ANN 📌 Tech stack: • Python (upcoming) What I learned today: • How library and framework can make our life so simple. Why this matters: Generative AI is not just about APIs. It’s about understanding models, pipelines, and real-world use cases. Sharing: 📸 Live class screenshot 📝 My self-prepared notes Building AI skills step by step — no shortcuts. Follow along if you’re serious about AI & engineering. #GenerativeAI #AIEngineering #TextToSpeech #JavaScript #Python #LLM #AIBuilder #BuildInPublic #LearningInPublic #TechCareers

🚀 Linear Regression From Scratch – Python Built a Linear Regression model from scratch to predict house prices using multiple features — no scikit-learn! ✅ Implemented gradient descent manually ✅ Added feature scaling ✅ Tracked cost over iterations Results: R² Score: 0.978 Cost dropped from 3.39e10 → 6.19e7 Trained parameters learned successfully Visualized cost convergence over iterations Learnings: How gradient descent works step-by-step Importance of feature scaling Evaluating model performance from scratch Looking forward to extending this to neural networks and advanced models! https://lnkd.in/dS5CtvxT #Python #MachineLearning #DataScience #LinearRegression #FromScratch

🚀 Master text extraction with #train-OCR a comprehensive pipeline for training OCR models. 📸 Features custom image preprocessing and deep learning architectures for high accuracy. 🛠️ Built with Python and TensorFlow to handle complex fonts and noisy datasets. 🔗 Check out the training scripts: https://lnkd.in/gUeDRDvS #OCR #DeepLearning #ComputerVision #Python #TensorFlow #AI #MachineLearning #OpenSource

Quietly building. Loud results coming. From engineering processes to engineering intelligence. Currently deep in Python, AI applications, and real-world problem solving. The transition isn’t easy — but it’s intentional. And it’s happening. #AI #Python #AIEngineering #CareerPivot #TechCareers #MachineLearning #LLM

Starting my 90-day AI mastery sprint today. The plan: → 60% technical learning (Python, ML, Deep Learning) → 40% building real solutions for businesses Following the math → code → deploy path. Day 1: Linear algebra + first Python code. Documenting the journey. Let's go. 🚀 #AI #MachineLearning #100DaysOfCode

Have you ever wondered how much of your LinkedIn feed is AI? I built a small Python utility that scans your feed and estimates likely AI usage in posts. Source code: https://lnkd.in/gytB8x7j I ran it on my own feed and visualized the results below. In my sample, 17% of posts looked highly AI-assisted, and another 39% showed moderate AI usage. That means over half of the posts in my feed used some level of AI. #AI #Python #DataAnalysis

Built a Neural Network from scratch using Python + CuPy. No TensorFlow. No PyTorch. Just math, matrices, and backpropagation. Implemented everything manually including: • Forward propagation • Backpropagation • Activation functions • Gradient descent • GPU acceleration using CuPy Sometimes the best way to understand AI is to build it from the ground up. https://lnkd.in/gDucwwXs #MachineLearning #DeepLearning #Python #AI

“I recently completed the ‘Python Using AI’ workshop conducted by AI for Techies.” Key takeaways: 1) Created interactive visualizations in minutes using AI. 2) Debugged Python code in seconds with AI support. 3)Generated Python code efficiently using AI tools. Looking forward to applying these skills in upcoming projects. #Python #ArtificialIntelligence #AI #Upskilling #Learning #TechSkills AI for Techies

🔍 NumPy Arrays vs Nested Lists in Python Choosing the right data structure directly impacts performance in data-driven applications. NumPy arrays provide optimized computation through vectorized operations and efficient memory handling — which is why they are widely used in Data Science, AI, and Machine Learning workflows. Key takeaways: • Performance comparison explained • Memory efficiency advantages • Practical use in numerical computing Read more info: https://lnkd.in/dNedRvtj #Python #DataScience #MachineLearning #SoftwareDevelopment #ArtificialIntelligence