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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.

HRDF SBL Claimable for Employers Registered with HRDF

HRDF claimable

Course Code: M454

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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.

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


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


Data Science TrainerDr. Aanand is a Full Stack Data Scientist who once had a torrid love affair with Physics. He has consulted and published in the area of Public Health, Electricity Markets, Telecom, BFSI, Advertising & Communication Strategies and Digital & Social Media Technologies. He has worked on assignments with international agencies such as International Monetary Fund, World Bank, Royal Netherland Embassy etc. besides MNCs like Tata Consultancy Services, Kie Square Consulting and several government organizations of national importance.

He regularly conducts general training programs in Python (Pandas, NumPy, SciPy, Matplotlib, Bokeh), R (dplyr, rstanarm, knitR, ggplot2), Data Visualization (Tableau, D3.js) and Machine Learnng (Reinforced Learning, Scikit Learn) and specialized training programs on Structural Equation Modeling and SAP Hana.

He holds a doctorate in Operations Research from Indian Institute of Management Ahmedabad and a post graduate in Physics from University of Mumbai. He has advanced training in mathematical programming including optimization, advanced multivariate data analysis, and simulation techniques. When he is not teaching or consulting he can be found meditating or heading for an adventurous trek.

Machine Learning TrainerAmir Othman is a software engineer by profession. Being educated in Bauhaus Universität Weimar and Hochschule Ulm, he brings experiences from different facades of the world.

With expertise in web technology, natural language processing and machine learning, he is a freelance data scientist. Some of his works include two international news aggregator and

He also holds an impressive port folio for data visualizations, primarily focusing on web based techniques.

R Statistics TrainerSultan Maideen has over 8 years of experience in Business Intelligence with Predictive Analytics and Data Visualization. His colleagues regard him as a self-motivated thought leader in building governed self-service BI/Analytical models in Tableau, QlikView, MicroStrategy, and Power BI.

He specializes in enhancing the quality of business strategy and helping companies improve top-line & bottom-line by devising creative solutions & managing stakeholders’ expectations successfully.

Fares Hasan Academically majored in Artificial Intelligence with a strong passion for machine learning and data science. Fueled by the drive to learn and the endurance for growth beyond the ordinary. His work with medical imaging in diagnosing tuberculosis using chest x-ray images has given him a purpose. Deriving values from data and solving real-life problems interests him and he always looking for new ways to contribute. Often said a man grows when he steps forward, fares have taken that step and his journey to the unicorn has started.

MachineLearning TrainerMohamad Mehdi Lotfinejad is an IT/IS Analyst and Solution Designer, IT Strategic Planning Consultant, Web Designer and Developer, Web Application Security Expert, eMarketing Consultant and Lecturer. He is also on the editorial board of the Internation Association of Academians, Canada as a technical manager.

MachineLearning TrSyed Muhammad Farrukh Akhtar has more than 15 years of experience analysis, designing, developing, integrating and managing large applications for diverse industries. He has experience working in Dubai, Pakistan, Germany and Malaysia, strong hands-on experience of software design, development and integration on different platform like IBM J2EE, Oracle and Microsoft .Net, Big data, Hadoop, Spark, HBase, Hive, Sqoop, Flume and NoSQL. He also has expertise in Machine Learning/ Deep Learning with Tensor Flow, Keras and Python, excellent skills in React, Ionic 2, Angular 2, Mobile Apps with React Native and Node.js.

He is highly knowledgeable in object oriented software development, requirements analysis, and database design. Possess deep understanding of Open Source technologies’ applicability in emerging business areas. He possesses excellent knowledge in Rational Unified Process (RUP); Rational Software Architect; data modeling and mapping; and extensible system design using the UML and Visio. Professional experience on J2EE, JMS, Web Sphere, Oracle, Spring, Hibernate, Struts and 3-Tier Web-based Applications Development.

Basic Tableau Trainer Ghazaleh Babanejad has received her Ph.D from University Putra Malaysia in Faculty of Computer ‎Science and Information Technology. She is working on recommender systems in the ‎field of skyline queries over Dynamic and Incomplete databases for her PhD thesis. She is also working ‎on Data Science field as a trainer and Data Scientist. She worked on Machine Learning and Process ‎Mining projects. She also has several international certificates in Practical Machine Learning (John ‎Hopkins University) Mining Massive Datasets (Stanford University), Process Mining (Eindhoven ‎University), Hadoop (University of San Diego), MongoDB for DBAs (MongoDB Inc) and some other ‎certificates. She has more than 5 years of experience as a lecturer and database administrator.

Anas Arram has over 5 years of experience in Data mining and optimization with a strong computing science in machine learning, data mining, combinatorial optimization problems and optimization algorithms. His areas of interest include: Operations Research, artificial intelligence and evolutionary computation, machine learning algorithm, text mining, combinatoric algorithms, database management, statistical analysis and data mining techniques.

Python Scikit TrainerJack Goh is a machine learning engineer at Coqnitics, Kuala Lumpur Malaysia. His job scope included research and development in computer vision recognition using ML/DL object detection and classification models. He has experience in using Django, PHP, CSS, HTML and JavaScript.

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