Learn Python basics
Learn math fundamentals: linear algebra, calculus, probability, statistics
Learn data handling with NumPy, pandas, and Matplotlib
Learn core ML concepts: supervised, unsupervised, and reinforcement learning
Learn model evaluation: train/validation/test splits, cross-validation, metrics
Learn feature engineering and data preprocessing
Learn common algorithms: linear regression, logistic regression, decision trees, random forests, SVM, k-NN, k-means
Learn gradient descent and optimization
Learn overfitting, underfitting, bias, and variance
Learn scikit-learn for practical ML workflows
Build small projects with real datasets
Practice on Kaggle or similar platforms
Learn deep learning basics with PyTorch or TensorFlow
Read ML papers and blog posts
Study deployment basics: APIs, model serving, and monitoring
Keep improving with projects, experiments, and iteration