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Artificial Intelligence
- Step 1: Learn the Fundamentals
- Learn Python (focus on NumPy, Pandas, Matplotlib)
- Study math basics: Linear Algebra, Probability, Calculus
- Understand AI concepts: Machine Learning vs Deep Learning
- Step 2: Dive into Machine Learning
- Learn Supervised and Unsupervised Learning algorithms
- Use Scikit-learn for real-world ML tasks
- Practice with datasets like Titanic, Iris, and MNIST
- Step 3: Deep Learning Specialization
- Learn about Neural Networks, CNNs, RNNs
- Use frameworks like TensorFlow and PyTorch
- Train models on images, text, or time series data
- Step 4: Projects & Deployment
- Build end-to-end AI projects (e.g., Chatbot, Image Classifier)
- Learn model deployment (Flask, Streamlit)
- Showcase work on GitHub or portfolio
