PyQdeck LogoPyQdeckSubjectsSyllabusBlog

Basic of ML

100618

6 modules · 22 topics

Module 1Introduction to Machine Learning and Fundamentals5 topics
  • Introduction to Machine Learning
  • Supervised Learning, Unsupervised Learning and Reinforcement Learning
  • Machine learning basics — Capacity, Overfitting and Underfitting
  • Hyperparameters and Validation sets
  • Bias and Variance
Module 2Bayesian Concepts and Decision Trees5 topics
  • Bayes Theorem and Concept Learning
  • Maximum Likelihood classifier
  • Bayesian Belief Networks
  • Decision trees — Univariate and Multivariate decision tree
  • Network equivalent of decision trees, Overfitting and Evaluation
Module 3Instance-Based Learning and Support Vector Machines5 topics
  • Logistic Regression
  • Support Vector Machine (SVM)
  • Kernel function and Kernel SVM
  • Instance-based Learning — k-Nearest Neighbour, Locally weighted Regression, Radial Basis Functions
  • Case Based Learning
Module 4Clustering3 topics
  • Clustering — k-means
  • Adaptive hierarchical clustering
  • Gaussian mixture model
Module 5Neural Networks3 topics
  • Neural network — Perceptron
  • Multilayer network
  • Back propagation
Module 6Applications and Case Studies of Machine Learning1 topics
  • Applications of machine learning (Case studies) — Business Analytics, Natural Language Processing, Computer Vision, Internet of Things, Bio-informatics etc.
Theory