A Machine Learning Library for Training Models

→ Maths Required (Before Phase 0)

Goal: Understand the foundational maths behind ML algorithms

Topics Covered:

• Basic Statistics (mean, median, mode, variance)
• Probability Concepts
• Linear Algebra (vectors, matrices, dot product)
• Derivatives and Gradient
• Distance Metrics (Euclidean, Manhattan)
• Concept of Error and Cost Functions

→ Phase 0: Foundations of Machine Learning

Goal: Understand what Machine Learning is and where it is used

Topics Covered:

• What is Machine Learning?

• Difference: AI vs ML vs Deep Learning

• Types of ML: Supervised, Unsupervised, Reinforcement

• Core ML Concepts:

  → Features (X) and Target (y)

  → Model, training, testing, prediction

• Where Scikit-learn fits into the ML workflow

• Why learn Scikit-Learn for real-world projects