Machine Learning for Engineers

Bridge the gap between complex calculus and production-ready Python code. You will master core algorithms through hands-on implementation while reinforcing the mathematical laws essential for model accuracy. * **Translate complex math** into functional Python code * **Apply core theorems** like Bayes and Chain Rule accurately * **Build robust models** using Scikit-Learn and NumPy * **Debug algorithmic errors** by identifying underlying mathematical flaws

6 sections ยท 24 lessons

Course outline

Linear Models

  1. Linear Regression
  2. Multiple Regression
  3. Gradient Descent
  4. Learning Rates

Model Evaluation

  1. Loss Functions
  2. Bias Variance
  3. Cross Validation
  4. Regularization

Classification

  1. Logistic Regression
  2. Decision Trees
  3. Random Forests
  4. Classification Metrics

Kernel Methods

  1. Support Vectors
  2. Soft Margins
  3. Kernel Trick
  4. Radial Basis

Unsupervised Learning

  1. K-Means Clustering
  2. Hierarchical Clustering
  3. Principal Components
  4. Feature Scaling

Neural Networks

  1. Perceptrons
  2. Activation Functions
  3. Forward Propagation
  4. Backpropagation

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