Learn Python thoroughly
Learn mathematics for AI: linear algebra, calculus, probability, statistics
Learn data structures and algorithms
Learn SQL and database basics
Learn data cleaning and preprocessing
Learn machine learning fundamentals
Learn deep learning fundamentals
Learn common ML libraries: NumPy, pandas, scikit-learn, PyTorch, TensorFlow
Build projects with real datasets
Practice model training, evaluation, and tuning
Learn feature engineering
Learn model deployment basics
Learn APIs and backend basics
Learn cloud platforms: AWS, GCP, or Azure
Learn MLOps tools and practices
Learn version control with Git and GitHub
Learn containerization with Docker
Learn basic Linux and command line skills
Study AI concepts: NLP, computer vision, recommendation systems, generative AI
Read research papers and follow AI trends
Participate in Kaggle competitions
Create a portfolio of AI projects
Write clean, maintainable code
Practice debugging and performance optimization
Learn responsible AI, ethics, and bias awareness
Apply for internships, junior roles, or freelance projects
Keep learning and updating your skills regularly