Machine Learning Skills & Math Foundations

🧠 Core Mathematical Foundations

• Linear Algebra: Vectors, matrices, eigenvalues, SVD
• Calculus: Derivatives, gradients, chain rule, optimization
• Probability & Statistics: Bayes’ theorem, distributions, hypothesis testing
• Optimization: Convex functions, gradient descent variants
• Geometry & Similarity: Cosine similarity, Jaccard index

🛠️ Practical & Technical Skills

• Programming: Python (NumPy, pandas, scikit-learn, TensorFlow, PyTorch)
• Data Handling: Cleaning, transformation, visualization
• Model Evaluation: Accuracy, precision, recall, F1-score, cross-validation
• Software Engineering: Git, testing, deployment
• Domain Knowledge: Tailoring models to specific industries

🚀 Bonus: Soft Skills That Set You Apart

• Critical Thinking: Choosing and interpreting models wisely
• Communication: Explaining concepts to non-technical audiences
• Curiosity: Staying updated with new tools and techniques