Machine Learning

Machine learning, a technology which our industry is adopting on very large scale. That’s also increased the demand of professionals in this sector. This technology is being used with web search, ads posting, credit scoring, stock trading, healthcare and any such area where past reading and future prediction is required for smart move. This makes the software applications to be more accurate towards a prediction without being explicitly programmed. It builds algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.


Can access computer system and make use of internet to perform search over Google.

Need to Prepare?

Knowledge of Matrix will be appreciated.Prior programming experience is not required.

Tools Expected:-

Windows OS based PC, Smart phone with Internet, Notebook and Pen

Tools Provided (for the session):-

MATLAB, OCTAVE GUI, OCTAVE CLI, and Reference Material


Supervised & Unsupervised learning, Linear and Logistics Regression, MATLAB


This workshop is all about introduction of the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.


  • Programming: Linear Regression
  • Programming: Logistic Regression
  • Programming: Regularized Linear Regression and Bias/Variance
  • Error Analysis



2 Days (7 hours each including 1-hour lunch break)


Day 1

Session 1-  (03:30 hrs)

  • Welcome to Machine Learning!
  • Machine Learning Honor Code
  • What is Machine Learning?
  • Supervised Learning
  • Unsupervised Learning
 Linear Regression with One Variable

    [ Linear regression predicts a real-valued output based on an input value.]

  • Model Representation
  • Cost Function
  • Cost Function – Intuition I
  • Cost Function – Intuition II
  • Gradient Descent
  • Gradient Descent Intuition
  • Gradient Descent For Linear Regression
Linear Regression with Multiple Variables
  • Matrices and Vectors
  • Installing MATLAB
  • Installing Octave on Windows
  • Multiple Features
  • Gradient Descent for Multiple Variables
  • Gradient Descent in Practice I – Feature Scaling
  • Gradient Descent in Practice II – Learning Rate
  • Features and Polynomial Regression
  • Normal Equation
  • Normal Equation Noninvertibility
Session 2- (02:30 hrs)
Octave / Matlab
  • Basic Operation
  • Moving Data Around
  • Computing on Data
  • Plotting Data
  • Control Statements: for, while, if statement
  • Vectorization
  • Programming: Linear Regression
Logistic Regression

         [Logistic regression is a method for classifying data into discrete outcomes.]

  • Classification
  • Hypothesis Representation
  • Decision Boundary
  • Cost Function
  • Simplified Cost Function and Gradient Descent
  • Advanced Optimization
  • Multiclass Classification: One-vs-all
Session Recap
Day 2
Session 1- (03:30 hrs)

         [Prevent models from overfitting the training data.]

  • The Problem of Overfitting
  • Cost Function
  • Regularized Linear Regression
  • Regularized Logistic Regression
  • Programming: Logistic Regression
Advice for Applying Machine Learning

[We Apply machine learning in practice, and discuss the best ways to evaluate performance of the learned models. ]

  • Deciding What to Try Next
  • Non-linear Hypotheses
  • Model Representation I
  • Model Representation II
  • Evaluating a Hypothesis
  • Model Selection and Train/Validation/Test Sets
  • Diagnosing Bias vs. Variance
  • Regularization and Bias/Variance
  • Learning Curves
  • Deciding What to Do Next Revisited
  • Programming: Regularized Linear Regression and Bias/Variance
Session 2- (02:30 hrs)
Machine Learning System Design

      [understand the performance of a machine learning system with multiple parts]

  • Prioritizing What to Work On
  • Error Analysis
  • Error Metrics for Skewed Classes
  • Trading Off Precision and Recall
  • Data For Machine Learning
Session Recap
Zonal Round of SkillThon
  • Competition
  • Certificate distribution and acknowledgement


INR 1200 (GST exclusive) Per Participation