AI/ML Roadmap 🤖
📂 Step 1: Math Foundation
∟📂 Linear Algebra (Vectors, Matrices, Eigenvalues)
∟📂 Probability & Statistics (Distributions, Bayes, Sampling)
∟📂 Calculus (Derivatives, Gradients, Chain Rule)
∟📂 Optimization (Gradient Descent, Cost Functions)

📂 Step 2: Computer Science Basics
∟📂 Algorithms & Data Structures
∟📂 Time and Space Complexity
∟📂 OOPs & Design Principles

📂 Step 3: Programming for ML
∟📂 Python / R / Julia (pick one)
 ∟📂 Numpy, Pandas
 ∟📂 Data Visualization (Matplotlib, Seaborn, Plotly)
 ∟📂 Data Preprocessing & Handling

📂 Step 4: Core Machine Learning
∟📂 ML Theory (Bias-Variance, Underfitting/Overfitting)
∟📂 Supervised Learning
∟📂 Unsupervised Learning
∟📂 Model Evaluation (Accuracy, ROC, Confusion Matrix)
∟📂 Scikit-Learn or Equivalent

📂 Step 5: Deep Learning
∟📂 Neural Networks Fundamentals
∟📂 Activation Functions, Loss Functions
∟📂 CNNs, RNNs, LSTMs
∟📂 Frameworks: TensorFlow or PyTorch

📂 Step 6: Specializations
∟📂 NLP (Text Classification, Transformers, BERT, LLMs)
∟📂 Computer Vision (Image Classification, Detection)
∟📂 Time Series Forecasting
∟📂 Recommendation Systems

📂 Step 7: MLOps & Deployment
∟📂 Model Packaging (Pickle, ONNX)
∟📂 Deployment (Flask, FastAPI, Streamlit)
∟📂 CI/CD & Cloud (AWS/GCP, Docker, MLflow)

📂 Step 8: Projects & Practice
∟📂 Kaggle Competitions
∟📂 Research Papers (arXiv, Papers with Code)
∟📂 GitHub Portfolio
  ∟📂 Resume + LinkedIn Optimization
   ∟✅ Apply for AI/ML Jobs or Internships

React "❤️" For More