Prof. Hetal Gaudani

Prof. Priyang Bhatt

Machine Learning : Tools and Techniques

About Course

The Objective is to introduce to various Machine Learning Techniques and algorithms like, Data Preprocessing, Regression Analysis, Supervised and Unsupervised Learning etc. with help of Different data mining Tools and python programming with their real word applications, as well as general techniques related to analyzing and handling large datasets. This training program will provide an applicative and hands-on approach to attendees in learning common machine learning techniques and tools. Several machine learning libraries and datasets publicly available will be used to illustrate the application of these algorithms. The emphasis will be thus on machine learning algorithms and applications, with the applied or working explanation of the underlying principles.

Expected Outcome:

  • A better understanding of basic programming and data analytics constructs
  • Having a better understanding of various data analysis toolkits and technologies
  • Ability to use a pre-existing machine learning models
  • Ability to develop Machine learning models
  • Will have a better understanding of advanced techniques like Dimensionality Reduction, Deep Learning etc.
  • Will learn new application areas of AI and Machine Learning

About Faculty:

Target Audience:

second,third and final year students

Duration:25 Hour


Sr No Content
1 Python basic syntax, data visualization, data loading, data preprocessing, Introduction to different data mining Tools
2 Introduction of Machine learning. Supervised and unsupervised learning
3 Regression : simple regression ,multivariate regression,logistic regression
LAB: house price prediction using linear regression, diabetic prediction using logistic regression
4 Classification: KNN,Bayesian Learning,Decision Trees ,SVM,Neural network
LAB: recommendation system using KNN, spam email classification using Bayesian, predict whether a bank currency note is authentic or not using svm, Zoo animal classification using decision tree )
5 Clustering : k-means, GMM
LAB: Speaker recognition using GMM and KNN

6 Dimensionality Reduction Principal Components Analysis, LDA
(LAB:Face recognition using PCA and LDA)
7 Deep Learning Convolution Neural Network, RNN