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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.

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