These are essential because ML engineers implement and optimize models.
Python (most important)
C or Java (for performance-heavy systems)
SQL (for querying data)
Libraries & frameworks:
NumPy
Pandas
Scikit-learn
PyTorch
TensorFlow
Machine learning algorithms rely heavily on math.
Key topics:
Linear Algebra (vectors matrices)
Probability & Statistics
Calculus (gradients optimization)
Optimization techniques
Machine Learning & AI Concepts
You must understand algorithms not just run them.
Important topics:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Feature Engineering
Model Evaluation (accuracy precision recall)
Bias-variance tradeoff
Popular algorithms:
Linear/Logistic Regression
Decision Trees
Random Forest
Gradient Boosting
Support Vector Machines
Neural Networks