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 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.

Course Objectives:


7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Introduction to Machine Learning
    1. What is Machine Learning
    2. Machine Learning in Real life
    3. Types of Machine Learning
    4. Key ML Models
    5. Installing Weka
    6. Load Dataset to Weka
    7. Build Your First Classifier
  2. Classification
    1. What is Classification?
    2. K-Nearest Neighbours (KNN)
    3. Support Vector Machine (SVM)
    4. Naive Bayes
    5. Decision Tree (DT)
  3. Regression
    1. What is Regression?
    2. Linear Regression
    3. Support Vector Regression
    4. K-Nearest Neighbour Regression
  4. Ensemble Methods
    1. What is Ensemble Methods?
    2. Bagging
    3. Random Forest
    4. Stacking
  5. Clustering
    1. What is Clustering?
    2. K-Means Clustering
    3. Hierarchical Clustering
  6. Neural Network
    1. What is Neural Network?
    2. Multilayer Perceptron Classifier
  7. Problem Solving through Machine Learning
    1. Problem Definition
    2. Data Conceptualization
    3. Data Gathering
    4. Feature Engineering
    5. Algorithm Spot Check
    6. Fine Tuning Model
    7. Pitfalls