Mugesh S. - Hands-on ML Projects with OpenCV - 2023.pdf
7 MB
đ5đ2â¤1đĨ1
AI/ML Roadmapđ¨đģâđģđžđ¤ -
==== Step 1: Basics ====
đ Learn Math (Linear Algebra, Probability).
đ¤ Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
đĸ Clean & Visualize Data (Pandas, Matplotlib).
đī¸ââī¸ Learn Core Algorithms (Linear Regression, Decision Trees).
đĻ Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
đĄ Understand Neural Networks.
đŧī¸ Learn TensorFlow or PyTorch.
đ¤ Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
đŗ Study Advanced Algorithms (Random Forest, XGBoost).
đŖī¸ Dive into NLP or Computer Vision.
đšī¸ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
đ¨ Create real-world projects.
đ Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
==== Step 1: Basics ====
đ Learn Math (Linear Algebra, Probability).
đ¤ Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
đĸ Clean & Visualize Data (Pandas, Matplotlib).
đī¸ââī¸ Learn Core Algorithms (Linear Regression, Decision Trees).
đĻ Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
đĄ Understand Neural Networks.
đŧī¸ Learn TensorFlow or PyTorch.
đ¤ Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
đŗ Study Advanced Algorithms (Random Forest, XGBoost).
đŖī¸ Dive into NLP or Computer Vision.
đšī¸ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
đ¨ Create real-world projects.
đ Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
đ15â¤4