Machine Learning Lab Manual

University of Science and Technology Lab Manual

• Introduction to Machine Learning
• Lab Manual
• Prepared by: Prof. Noureldien A. Noureldien
• October 2024

Table of Contents

• Week #1: Python Build-in functions and the Math Module (page 3)
• Week #2: NumPy Module (page 14)
• Week #3: CSV Files (page 27)
• Week #4: Pandas Package (page 41)
• Week #5: Data Preprocessing with Pandas (page 61)
• Week #6: Modelling with Scikit-learn (page 87)
• Week #7: Dataset Feature Selection Techniques (page 100)
• Week #8: Building Supervised Learning Classification Model (page 113)
• Week #9: Building Learning Regression Models (page 122)

Lab (1): Python Build-in- Functions and the Math Module

• Objectives: Introduce basic machine learning concepts, real-world problems, and different algorithms
• Outcomes: Describe basic concepts of machine learning, various algorithms, and evaluating performance of machine learning algorithms. Apply Machine learning to learn, predict and classify real-world problems. Students will acquire multidisciplinary skills.
• Lab (1): Learning Outcomes Details: Use Python build-in functions and write C code to manipulate math module functions.

Lab (2): NumPy Module

• Description: This lab describes the NumPy module, used for creating and manipulating arrays in Python.
• Learning Outcomes: Use Python NumPy module functions to write codes that manipulate arrays.

Lab (3): CSV Files

• Description: This lab goes over reading and writing from/to a CSV file.
• Learning Outcomes: Demonstrate reading and writing data from and to a CSV file using Python.
• Details: What a CSV is, structure of a CSV file in Python, reading a CSV file in Python (csv.reader), and writing a CSV file in Python.

Lab (4): Pandas Package

• Description: This lab provides basic knowledge of Python's Pandas package.
• Learning Outcomes: Use Pandas package to manipulate and explore data sets and write codes implementing Pandas packages.

Lab (5): Cleaning of Data Using Pandas

• Description: Data cleaning using the Pandas package.
• Learning Outcomes: Use Pandas package functions to clean data.

Lab (6): Understand and Visualize Your Data

• Description: Instructions on how to understand and visualize data using Python.
• Learning Outcomes: Understand and visualize the machine learning dataset. Write Python codes that implement how to understand and visualize the dataset.

Lab (7): Data Preprocessing and Machine Learning Modeling using Scikit-learn

• Description: Data preprocessing and machine learning modeling using Scikit-learn library.
• Learning Outcomes: Perform data preprocessing and machine learning modeling using Scikit-learn library, Write Python codes implementing Scikit-learn tools for data preprocessing and modeling.
• Details: Machine learning basics, scikit-learn features, and different supervised machine learning techniques(ex: Binarization, Standardization, Scaling).

Lab (8): Dataset Feature Selection Techniques

• Description: Methods on how to apply feature selection techniques to datasets.
• Learning Outcomes: Understand feature selection techniques to datasets; write Python codes that apply feature selection techniques to datasets.

Lab (9): Building Supervised Machine Learning Classification Model

• Description: Details on how to build supervised machine learning classification models.
• Learning Outcomes: Understanding steps of building supervised machine learning models; writing Python codes to implement supervised machine learning classification models.

Lab (10): Building Learning Regression Models

• Description: Basic knowledge on how to build supervised machine learning regression models.
• Learning Outcomes: Implement supervised machine learning regression models, writing Python codes to implement supervised machine learning regression models.

Additional information

• Exercise 1, Exercise 2, etc.: Specific exercises related to the material in each lab.
• Modules: The Python modules imported for each exercise (e.g., pandas, numpy, matplotlib, stats).
• Data Analysis Techniques: Summary operators, descriptive statistics, and specific feature extraction techniques demonstrated in each exercise (e.g., plotting histograms and density plots).