Learn Python basics
Learn math fundamentals: linear algebra, calculus, probability, statistics
Learn data handling with NumPy, Pandas, and Matplotlib
Learn core ML concepts: supervised learning, unsupervised learning, overfitting, underfitting
Learn common algorithms: linear regression, logistic regression, decision trees, random forests, SVM, k-means, PCA
Learn model evaluation: train/test split, cross-validation, accuracy, precision, recall, F1, ROC-AUC
Learn feature engineering and preprocessing
Learn scikit-learn for practical ML workflows
Build small projects with real datasets
Practice on Kaggle and similar platforms
Read and implement ML papers and tutorials
Learn basics of deep learning with PyTorch or TensorFlow
Study deployment basics: APIs, model serving, Docker
Keep improving by debugging models and analyzing errors
Follow a consistent study plan and practice regularly