Learning machine learning in 100 days is absolutely achievable with the right roadmap and consistency. Start by building a strong foundation in mathematics, statistics, and Python programming. Gradually move to core machine learning concepts like supervised and unsupervised learning, model evaluation, and algorithms such as linear regression, decision trees, and clustering.

As you progress, focus on hands-on projects using real-world datasets and popular tools like TensorFlow and Scikit-learn. Dedicate time to understanding data preprocessing, feature engineering, and model optimization. In the final phase, work on advanced topics like deep learning, NLP, and deployment.

By following a structured 100-day plan, practicing daily, and building a strong project portfolio, you can become job-ready in machine learning and open doors to exciting career opportunities.