Product Description


The objective of this course is to cover the following –  basics of Machine learning, explain the algorithms in supervised and unsupervised learning with hands on examples, give an introduction to ANN. By end of the course students would be able to write programs using Machine learning algorithms.

Course Content:

Machine Learning Basics

  • Data mining and machine learning, Key Terminology in ML, Key Tasks, Bias and variance, over-fitting and under-fitting.  Input: Concepts, instances, and attributes, sparse data, attribute types, missing values, inaccurate values
  • Supervised Learning
  • Classification
  • Classifying with k-Nearest Neighbours, concept of distance, data normalization – Hands-On Example*
  • Decision Trees, Entropy, Information Gain, ID3 algorithm to build the Decision Tree, Gini co-efficient – Hands-On Example*
  • Brief Intro to Probability and Conditional Probability Theory, Bayesian Decision Theory and Bayes Rule, Classifying with Bayesian Decision Theory – Hands-On Example*
  • Introduction to hyperplane Basics, Classification using Maximal Margin Classifier, Linear and non Linear Models, Support vector classifier, Support vector machines, Kernel Functions – Hands-On Example*
  • Improving classification, Bagging, Boosting, Meta Algorithms / Ensembles, Adaptive Bootstrapping, AdaBoost Meta Algorithm, Algorithm performance testing metrics – ROC and AUC
  • Regression
  • Forecasting Numeric Values with Regression – Hands-On Example*
  • Linear, multiple, Logistic and logarithmic regression, Locally Weighted Linear Regression – Hands-On Example*
  • Tree-based regression – Hands-On Example*
  • Classification and Regression Tree (CART) Algorithm – Hands-On Example*

Unsupervised Learning

  • K-means clustering algorithm – Hands-On Example*
  • Association analysis with the Apriori algorithm – Hands-On Example*
  • Frequent Pattern Algorithm using FP-Trees – Hands-On Example*
  • Principal Component Analysis (PCA) for dimensionality reduction – Hands-On Example*
  • Singular Value Decomposition for dimensionality reduction – Hands-On Example*

Artificial Neural Networks

  • Introduction to Artificial Neural Networks and basics
  • Perceptron, Activation Functions, Multilayer Perceptron, Back Propagation
  • Unsupervised Learning: Self Organizing Maps (SOM) algorithm, Radial Basis Function (RBF) Network
  • Supervised Learning: Auto-encoders, Hopfield Networks, Boltzmann machines, Restricted Boltzmann Machines, Spiking Neural Networks

Duration: 40 Hours

Target Audience: Working professionals, Students and others interested in Machine learning area.

Prerequisite: Good understanding of basics of Mathematics and Exposure to Python

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