Course details

Practical Machine Learning

A brief summary

This course will be the introduction of Machine Learning and some of its most popular algorithms researchers and professionals used nowadays. After completing the course an individual, will be able to use machine learning in real life problem as this course will ensure every individual gets enough practice and at the end, everyone will implement machine learning techniques with real world data. If anyone interested, they will also be able to publish paper in renowned conferences with the data.

Course highlight

  • Strong Base in ML
  • Supervised Learning focused on Classification
  • Unsupervised Learning (Clustering)
  • Performance Evaluation
  • Optimization of Classifiers

What you will learn

They will be able to apply machine learning technique in real world data, learn a bunch of classification and clustering technique, how to evaluate the performance of these algorithm, what are the optimization technique and how can we improve the performance of a classifier.


01

Introduction to Machine Learning and Probability

Fundamental discussion on what is machine learning and there will be an overview of probability theories that will be necessary to go ahead with the course

02

Bayesian Classifiers

Detail discussion on different classification schemes and Naïve Bayes classifier.

03

Classifier Evaluation Technique

How you will know that you have really a good classifier, what are the performance measures, what different performance measure really mean

04

Distance based classifier

Discussion on k nearest neighbor classification technique, Linear Discriminant Analysis

05

Maximum Margin Classification Technique

Discussion on what is maximum margin classification and how to obtain the margin

06

Feature Selection Techniques

Detail discussion on different feature selection technique, some modern tricks to find best features

07

Tree Based Classifier

What are tree based classifiers and different tree based classification technique

08

Clsutering for unsupervised data

how we can find pattern in unsupervised data

09

Hierarchical Clustering and optimal cluster selection

What is hierarchical clustering and different ways to create hierarchy and how to identify the number of clusters

10

4 -5 Assignments

These assignments will be on small datasets

11

1 project

A number of dataset will be provided. An individual can bring his own dataset too but he needs to get the data pre approved by the instructor


Next Batches

No upcoming batches available.
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