Machine Learning Lab Manual

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Questions and Answers

What is the output shape of a numpy array created using three lists, each containing four elements?

(3, 3)

(2, 6)

(4, 3)

(3, 4) (correct)

Which numpy method provides information on the number of elements along each axis of an array?

dimensions()

size()

length()

shape() (correct)

When creating a numpy multi-dimensional array, what notation should be used to include multiple lists?

{} inside np.array()

[] inside np.array() (correct)

|| inside np.array()

() inside np.array()

Given the numpy array created from 5 lists, each containing 3 elements, what would be the output of sample_array.shape?

(5, 3) (C) Signup and view all the answers

What does Axis 0 represent in the context of a numpy array?

One-dimensional arrays (B) Signup and view all the answers

What must be specified when reading a text file into a DataFrame?

The separator using the sep argument (B) Signup and view all the answers

How do you specify which sheet to read from an Excel file that contains multiple sheets?

With the sheet_name argument (A) Signup and view all the answers

Which code snippet correctly exports a DataFrame as a text file with specified separation?

df.to_csv('diabetes_out.txt', sep=' ') (B) Signup and view all the answers

What does the index=False argument do when saving a DataFrame to a CSV file?

It excludes the DataFrame's row index from the output file (A) Signup and view all the answers

Which function is used to read an Excel file into a DataFrame?

pd.read_excel() (D) Signup and view all the answers

What is the output of the expression df.shape?

(768, 9) (B) Signup and view all the answers

How can you obtain only the number of columns from a DataFrame's shape?

df.shape[1] (B) Signup and view all the answers

What does the .isnull() method return when applied to a DataFrame?

A DataFrame indicating the presence of missing values (B) Signup and view all the answers

Which method would you use to count the total number of missing values in the entire DataFrame?

df.isnull().sum().sum() (C) Signup and view all the answers

What happens when you use the .copy() method on a DataFrame?

It creates a deep copy of the DataFrame. (C) Signup and view all the answers

Study Notes

University of Science and Technology Lab Manual

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

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).

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Description

Explore fundamental concepts and techniques in machine learning with this comprehensive lab manual. Covering various topics such as Python functions, NumPy, Pandas, and Scikit-learn, the manual guides you through practical applications in the field. Perfect for students looking to deepen their understanding of machine learning processes and algorithms.