PREREQUISITE : Basic Python or programming skills, and Basic mathematics + statistics (if any)

Lesson 1 Data Science & Machine Learning (6 hours)
  1.1. Types & anatomy of data
  1.2. Exploring data
  1.3. Data filtering & transformation
  1.4. Dimensionality reduction
Lesson 2 Traditional Machine Learning Techniques (6 hours)
  2.1. Concept learning & inductive learning hypothesis
  2.2. Decision tree learning
  2.3. Bayesian learning
  2.4. Nearest-neighbours algorithms
Lesson 3 Statistical Pattern Recognition (6 hours)
  3.1. Radial basis functions & kernel-based approaches
  3.2. Support vector machines
  3.3. Cluster analysis & unsupervised learning
  3.4. Linear models & regression problems
Lesson 4 Artificial Neural Networks (6 hours)
  4.1. Perceptrons
  4.2. Stochastic gradient descent algorithm
  4.3. Backpropagation & multi-layered networks
  4.4. Deep learning architectures & applications