100 Steps to Learn AI (A Journey from Curiosity to Mastery)

About this book

This book offers a transformative 100-step roadmap to holistic AI mastery across six phases, blending technical skills, ethics, and stewardship. Prologue: Awakening (Steps 1-5) begins with "What is intelligence?", contextualizing AI/ML/DL in daily life and inspiring vision. Phase I: Foundation (6-20) builds Python fluency with NumPy, Pandas, Matplotlib; revives Linear Algebra, Stats, Calculus as model language; sets up environments, version control, first predictive program, and early ethics. Phase II: ML Engine (21-40) covers supervised/unsupervised/RL algorithms (regression, KNN, trees, SVMs, clustering), Scikit-learn workflows, metrics (accuracy, F1, RMSE), bias-variance tradeoff, and end-to-end projects. Phase III: Deep Dive (41-60) explores perceptrons to backprop, TensorFlow/PyTorch, CNNs/RNNs/LSTMs, Dropout/Transfer Learning, GANs/VAEs, and NLP basics. Phase IV: Frontier (61-80) introduces Transformers/Hugging Face, RL (Q-Learning/DQNs), Docker/cloud deployment, Kaggle/ArXiv engagement, and portfolio building. Phase V: Integration (81-95) specializes in Vision/NLP/RL, masters MLOps, XAI (SHAP/LIME), bias/fairness, interdisciplinary fusion, and communication/mentoring for T-shaped professionals. Phase VI: Ascent (96-100) demands novel projects, open sharing, and "nurture the garden" stewardship. This narrative expedition cultivates wise practitioners to integrate AI responsibly into society