Introduction Machine Learning
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Follow on Instagram for UpdatesIntroduction Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Introduction Machine Learning (subject code DI04000061) as prescribed for Auto · Semester 4 divides the course into 5 units, covering 27 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI04000061)Unit 1 Introduction to Machine Learning
- ● Overview of Human Learning and Machine Learning ● Types of Machine Learning: Supervised
- Reinforcement ● Benefits Machine Learning ● Challenges of Machine Learning ● Applications of Machine Learning ● Tools and Technology for Machine Learning
Unit 2 Python libraries for Machine Learning
- Numpy ● Creating and Accessing Array: array()
- reshape() ● Stacking & Splitting: stack()
- split() ● Maths Functions: add()
- ● Statistics Functions: mean()
- var() Pandas ● Data structure: Series()
- DataFrame() ● Manipulating Data: isnull()
- max() drop()
- sort_values() Program Name: Diploma in Engineering Level: Diploma Branch: Computer Engineering / Computer Science and Engineering Subject Code : DI04000061 Subject Name: Introduction Machine Learning ● Working with CSV File: read_csv()
- to_csv() Matplotlib ● Functions: plot()
- savefig() Scikit-learn ● Key concepts and features ● Functions: load_*()
Unit 3 Preparing to Model and Evaluation
- ● Machine Learning activities ● Types of data in Machine Learning: Numerical (quantitative) data
- Categorical (qualitative) data ● Data quality and remediation ● Data Pre-Processing: Dimensionality reduction
- Feature subset selection ● Selecting a Model: Predictive/Descriptive ● Training a Model for supervised learning: Holdout method
- K-fold Cross-validation method ● Performance Evaluation of a model: confusion matrix ● Improving Performance of a model
Unit 4 Supervised Machine Learning
- Introduction to Supervised Learning ● Brief explanation of Supervised Machine Learning ● Learning Steps in Supervised Machine Learning ● Real world Applications/Examples of Supervised Machine Learning ● Advantage and disadvantage of Supervised Machine Learning Classification ● Define Classification ● Types of classification: Binary
- Multi-class ● k-Nearest Neighbor (kNN): Working of k-NN
- Need of k- NN algorithm
- Select value of k, advantage and disadvantage of k-NN algorithm Program Name: Diploma in Engineering Level: Diploma Branch: Computer Engineering / Computer Science and Engineering Subject Code : DI04000061 Subject Name: Introduction Machine Learning ● Support Vector Machines: Fundamentals concept Regression ● Define Regression analysis ● List types of Regression ● Linear regression: List types of Linear Regression
- mathematical equation of linear regression
- diagram of linear regression line (positive
- negative) ● Simple linear regression: Description
- objective ● List real-world examples of regression analysis
Unit 5 Unsupervised Machine Learning and Generative AI
- Introduction of Unsupervised Learning ● Need of unsupervised learning ● Working of unsupervised learning ● Types of unsupervised learning: Clustering
- Association rules ● Real-world examples of unsupervised Learning ● Supervised vs
- Unsupervised Learning Generative AI ● Define Generative AI ● Working of Generative AI ● Applications of Generative AI ● Case Study: Simulate port-wide hydrogen adoption, and forecast emission reductions with Generative AI and Green Hydrogen
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