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
Target Audience:	With fundamental concept of Machine Learning or have completed Machine Learning Fundamentals course
Registration:	Click here

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