AI/ML Roadmapπ¨π»βπ»πΎπ€ -
==== Step 1: Basics ====
π Learn Math (Linear Algebra, Probability).
π€ Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
π’ Clean & Visualize Data (Pandas, Matplotlib).
ποΈββοΈ Learn Core Algorithms (Linear Regression, Decision Trees).
π¦ Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
π‘ Understand Neural Networks.
πΌοΈ Learn TensorFlow or PyTorch.
π€ Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
π³ Study Advanced Algorithms (Random Forest, XGBoost).
π£οΈ Dive into NLP or Computer Vision.
πΉοΈ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
π¨ Create real-world projects.
π Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
==== Step 1: Basics ====
π Learn Math (Linear Algebra, Probability).
π€ Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
π’ Clean & Visualize Data (Pandas, Matplotlib).
ποΈββοΈ Learn Core Algorithms (Linear Regression, Decision Trees).
π¦ Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
π‘ Understand Neural Networks.
πΌοΈ Learn TensorFlow or PyTorch.
π€ Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
π³ Study Advanced Algorithms (Random Forest, XGBoost).
π£οΈ Dive into NLP or Computer Vision.
πΉοΈ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
π¨ Create real-world projects.
π Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml