- Python for ML – NumPy, Pandas, Matplotlib, Seaborn
- Math for ML – Linear Algebra, Calculus, Probability
- ML Foundations – Supervised, Unsupervised learning
- Algorithms – Regression, Classification, Clustering, Decision Trees
- Model Evaluation – Metrics, Bias-Variance, Cross-validation
- Deep Learning (Coming Soon) – Neural Networks, CNNs, RNNs
- Projects – Real-world datasets, EDA, and model building
To build a solid foundation in Machine Learning by combining theory, coding, and projects — and gradually transition toward AI/ML applications and research-level understanding.
Learning from resources like:
- CampusX ML Roadmap
- Coursera / Andrew Ng
- Kaggle Notebooks
- And many amazing YouTube educators ❤️
📅 Started: October 2025
🏁 Goal: Reach ML deployment-level confidence by mid-2026
LinkedIn - Kalpit Nagar