Machine Learning using Python
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Machine Learning using Python Syllabus — Unit-wise Content
The official GTU curriculum for Machine Learning using Python (subject code 3155202) as prescribed for AI & ML · Semester 5 divides the course into 7 units, covering 37 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 (3155202)Unit 1 Introduction to Machine Learning
- Overview of machine learning and its applications
- Types of machine learning techniques: supervised
- unsupervised, and reinforcement learning
Unit 2 Basics of python for Machine Learning
- Introduction to Python programming language
- Python libraries like NumPy and pandas for data manipulation
- Introduction to scikit-learn for machine learning implementations
Unit 3 Overview of Probability
- Statistical tools in Machine Learning
- Concepts of probability
- Random variables
- Continuous distributions
- Multiple random variables
- Central limit theorem
- Sampling distributions
- Hypothesis testing
- Monte Carlo Approximation
Unit 4 Supervised Learning Techniques and Algorithms
- Linear regression and logistic regression
- Support Vector Machines (SVM)
- Decision Trees and Random Forests
- k-Nearest Neighbors (k-NN)
Unit 5 Unsupervised Learning Techniques and Algorithms
- Challenges in Unsupervised Learning
- Preprocessing and Scaling
- Different Kinds of Preprocessing
- Data transformations and Scaling
- Dimensionality Subject Code : 3155202 Subject Name: Machine Learning using Python Reduction
- Feature Extraction
- Principal Component Analysis
- k-Means Clustering
- Agglomerative Clustering
Unit 6 Model Evaluation and Improvement
- Cross-Validation in scikit-learn
- Benefits of Cross- Validation
- k-Fold Cross-Validation and Other Strategies
- Evaluation Metrics
- Metrics for Binary Classification
- Metrics for Multiclass Classification
Unit 7 Natural Language Processing (NLP)
- Basics of NLP and text preprocessing
- Using NLP techniques for sentiment analysis and text classification
- Generative models
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