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Solving Problems with Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine Learning algorithms comb through data and identify patterns that are too complex to be discerned by the human mind. These patterns can then be used for decision making and action.

In this course we will use a Graphical User Interface (GUI) based Machine Learning tool (this course does not require any programming). We will work through examples and understand the the top regression, classification and ensemble machine learning algorithms.

We will also understand the process of problem solving through machine learning - including problem definition framework, data preparation, algorithm spot check, fine-tuning results and presenting outcome.


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Course Code: CRS-N-0040451

Course Booking

$298.00

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Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


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Course Details

Module 1 - Introduction to Machine Learning

  • What is Machine Learning 
  • Machine Learning in Real life 
  • Types of Machine Learning 
  • Key ML Models
  • Installing Weka
  • Load Dataset to Weka
  • Build Your First Classifier

Module 2 - Classification

  • What is Classification?
  • K-Nearest Neighbours (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes
  • Decision Tree (DT)


Module 3 - Regression

  • What is Regression?
  • Linear Regression
  • Support Vector Regression
  • K-Nearest Neighbour Regression

Module 4 - Ensemble Methods

  • What is Ensemble Methods?
  • Bagging
  • Random Forest
  • Stacking

Module 5 - Clustering

  • What is Clustering?
  • K-Means Clustering
  • Hierarchical Clustering

Module 6 - Neural Network

  • What is Neural Network?
  • Multilayer Perceptron Classifier

Module 7 - Problem Solving through Machine Learning

  • Problem Definition
  • Data Conceptualization
  • Data Gathering
  • Feature Engineering
  • Algorithm Spot Check
  • Fine Tuning Model
  • Pitfalls

Who Should Attend

This course is for both for:

Non programmers who wish to understand how Machine Learning should be applied for problem solving (without being bogged down by a particular programming language)
Programmers and Machine Learning practitioners who want to better understand how to apply the algorithms to real life problem solving

Prerequisite

This course does not require prior knowledge of any programming language. Practical experience with problem solving in a real business context will be helpful.

Trainers

Machine Learning TrainerGaurav Chaturvedi is the Lead Data Scientist with a leading bank in Singapore. He has 15 years of experience - as a Consultant leading transformational projects. His hands-on experience with data / technology as well as an appreciation of business enable him to problem solve effectively.

Cyber Security TrainerDr. Sarita Singh received her Ph.D. degree for her work done in the area of Information Security. She is the recipient of the prestigious Infosys fellowship for pursuing her Ph.D. Programme. She has more than twenty-five years of teaching and research experience in Singapore, Malaysia and India in the field of Programming, Information Security, Web-application Development, Computer Networks and Engineering related modules.

She has presented papers at several National and International Conferences and has written articles for magazines. She has authored text-books for Engineering courses as well.

Customer Reviews (1)

Will RecommendReview by Muhammad Faizal
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Nil (Posted on 7/23/2017)

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