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Questions and Answers
What is the output shape of a numpy array created using three lists, each containing four elements?
What is the output shape of a numpy array created using three lists, each containing four elements?
Which numpy method provides information on the number of elements along each axis of an array?
Which numpy method provides information on the number of elements along each axis of an array?
When creating a numpy multi-dimensional array, what notation should be used to include multiple lists?
When creating a numpy multi-dimensional array, what notation should be used to include multiple lists?
Given the numpy array created from 5 lists, each containing 3 elements, what would be the output of sample_array.shape?
Given the numpy array created from 5 lists, each containing 3 elements, what would be the output of sample_array.shape?
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What does Axis 0 represent in the context of a numpy array?
What does Axis 0 represent in the context of a numpy array?
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What must be specified when reading a text file into a DataFrame?
What must be specified when reading a text file into a DataFrame?
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How do you specify which sheet to read from an Excel file that contains multiple sheets?
How do you specify which sheet to read from an Excel file that contains multiple sheets?
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Which code snippet correctly exports a DataFrame as a text file with specified separation?
Which code snippet correctly exports a DataFrame as a text file with specified separation?
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What does the index=False argument do when saving a DataFrame to a CSV file?
What does the index=False argument do when saving a DataFrame to a CSV file?
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Which function is used to read an Excel file into a DataFrame?
Which function is used to read an Excel file into a DataFrame?
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What is the output of the expression df.shape?
What is the output of the expression df.shape?
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How can you obtain only the number of columns from a DataFrame's shape?
How can you obtain only the number of columns from a DataFrame's shape?
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What does the .isnull() method return when applied to a DataFrame?
What does the .isnull() method return when applied to a DataFrame?
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Which method would you use to count the total number of missing values in the entire DataFrame?
Which method would you use to count the total number of missing values in the entire DataFrame?
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What happens when you use the .copy() method on a DataFrame?
What happens when you use the .copy() method on a DataFrame?
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Study Notes
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).
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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.