Python.
C.
Java.
JavaScript.
Core ML Skills
Supervised and unsupervised learning.
Model training, validation, and evaluation.
Feature engineering and dimensionality reduction.
Handling imbalanced datasets.
Deep Learning Skills
Neural networks.
Model optimization.
Transfer learning and fine-tuning pretrained models.
Attention mechanisms.
ML Tools & Frameworks
TensorFlow and Keras.
Scikit-learn.
Pandas & NumPy for data manipulation.
Matplotlib & Seaborn for visualization.
Research-Oriented Skills
Explainable AI (XAI) methods like SHAP, LIME, surrogate models.
Experimental design for model comparison.
Data collection and preprocessing for custom datasets.
Writing manuscripts and presenting technical research findings.
Applied/Practical Skills
Building end-to-end ML pipelines.
Hyperparameter tuning .
Deployment basics.
Jupyter Notebook.
Google Colab.
Google Cloud.
Git / GitHub.
Overleaf.