RCSS Logo header

tagline one

current students

Applying Machine Learning Algorithms online course for students

Further to the Machine Learning Fundamentals course, a 15-hour online course focuses on applying Machine Learning algorithms to solve real life problems via data analytics will be available in November.

This online course consists of 6 lessons covering some basic techniques with examples via open source software data mining tools. The course will cover how data analytics and relevant machine learning algorithms will be able to provide insights and improve your decision-making on following types of daily problems:

Example              Types of Problem
Is it A or B                                Classification problem
Is this wired                              Anomaly detection problem
How much or how many            Regression problem
How is this organized                Clustering problem

 

Machine Learning Techniques webinar:

Date:

10 Nov (Tue), 12 Nov (Thu), 17 Nov (Tue),

19 Nov (Thu), 24 Nov (Tue) & 26 Nov (Thu)
Time:

14:30 – 17:00

*6 lessons with each lesson 2.5 hours (Total 15 hours)
Course Outline:

(10 Nov) Lesson 1 - Regression and Sequential Pattern

  • Regression problem introduction
  • Over fitting and generalization
  • Error Function
  • Linear regression


(12 Nov) Lesson 2 - Regression and Sequential Pattern

  • Logistic regression
  • Sequential pattern problem introduction
  • Abnormal detection


(17 Nov) Lesson 3 - Classification and Neural Network I

  • Linear Classifiers & Logistic Regression
  • Overfitting & Regularization in Logistic Regression
  • Decision Trees
  • Boosting
  • Handling Missing Data
  • Naive Bayes Classifier
  • Support Vector Machines


(19 Nov) Lesson 4 - Classification and Neural Network II

  • Basic Deep Neural Network
  • Convolution Neural Network
  • Generative adversarial network


(24 Nov) Lesson 5 - Clustering

  • Nearest Neighbor Search (KNN)
  • Clustering with k-means
  • Mixture Models


(26 Nov) Lesson 6 - Association rules and outlier detection

  • Apriori Algorithm
  • Isolation Forest
  • Minimum Covariance Determinant
  • Local Outlier Factor
  • One-Class SVM
Medium of Instruction: English
Pre-requisite:

With fundamental concept of Machine Learning or
have completed Machine Learning Fundamentals course
Target Audience: RPg, TPg, Ug students
Registration: Click here

Interested students are welcome to join this webinar. Don’t wait, enroll now!