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4G curriculum (older)

For students clearing pending subjects of the 4G scheme (7-digit subject codes, e.g. 4341602). Students admitted under the current scheme study the 5G curriculum.

4341603 · Semester 4 · 4G scheme

Fundamentals of Machine Learning

Notes and solutions following the Gujarat Technological University (GTU) syllabus for 4341603. Independent material — not issued by the university.

Information Technology

Materials

Syllabus

The official syllabus this material follows.

  • Syllabus

    9 pages

    FreeRead

Complete Book

The whole syllabus, unit by unit, written for the exam.

Problem Solver

Step-by-step solutions to typical problems.

PYQ Solutions

Past university papers, fully solved.

  • Solved past papers — detailed answers

    152 pages · English + ગુજરાતી

    Premium Preview · 7 pages

  • Solved past papers — short answers

    112 pages · English + ગુજરાતી

    Premium Preview · 4 pages

Syllabus units

Unit 1: Introduction to machine learning4 h · 8 marks
  • Overview of Human Learning and Machine Learning
  • Types of Machine Learning
  • Applications of Machine Learning
  • Tools and Technology for Machine Learning
Unit 2: Preparing to Model6 h · 12 marks
  • Machine Learning activities
  • Types of data in Machine Learning
  • Structures of data
  • Data quality and remediation
  • Data Pre-Processing
Unit 3: Modeling and Evaluation8 h · 12 marks
  • Selecting a Model
  • Training a Model for supervised learning:
  • Model representation and interpretability
  • Evaluating performance of a model
  • Improving Performance of a model
Unit 4: Supervised Learning - Classification and Regression10 h · 14 marks
  • Introduction to supervised learning.
  • Classification Model
  • Learning steps
  • Classification Algorithms
  • Regression
Unit 5: Unsupervised Learning8 h · 12 marks
  • Supervised vs. Unsupervised Learning
  • Applications of unsupervised learning
  • Clustering
  • Finding pattern using Association Rule
Unit 6: Python libraries for Machine learning6 h · 12 marks
  • Pandas
  • Numpy
  • Matplotlib
  • Scikit-learn

About this course

Machine learning focuses on the use of data and algorithms to perform learning similar to the way human learns. To solve recent problems in IT domain it is important to understand the need of machine learning and apply machine learning methods in efficient ways. Every student of Information Technology must therefore understand the blue prints of machine learning approaches and must be able to apply learning methods on available datasets. This course will help students to build up core competencies in understanding machine learning approaches and students will be able to design and train machine learning modes for various use cases.