Python Assignment - Computer Science Assignment Help

Assignment Task

PYTHON ASSIGNMENT
Instructions
MNIST number dataset a set of 70,000 small images of digits handwritten by high school students and employees of the US Cen? sus Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the “hello world” of Machine Learning: whenever people come up with a new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns Machine Learning tackles this dataset sooner or later.
Instructions to explore this dataset are:

1. Use Jupyter Notebook for interactive practice of Python and related Machine Learning packages.?
a. For installing jupyter notebook, could install anaconda first, as Anaconda is the most widely used Python distribution for data science and comes pre-loaded with all the most popular libraries and tools.

b. Create virtual environment for each python project

c. Familiarize yourself with cells in jupyter notebook and practice mixing texts and python coding.

2. Always refer to textbook ‘hands-on machine learning with Scikit-Learn, Keras & TensorFlow‘ for coding help.

3. Specific tasks include

a. download dataset.

 b. explore the dataset and output information include?

i. how many images ii. how many features and the range of feature values (e.g., histogram of the data value) iii. how many categories/labels (discrete or continuous type) iv. visualize randomly selected samples within each category (feel the variance of the data) v. visualize more data samples to see whether there are bad data samples need to be removed. c. do more data manipulation? i. Use two dimensional reduction algorithms, PCA and t-SNE, to reduce the MNIST dataset down to two dimensions and plot each result using Matplotlib. You can use a scatterplot using 10 different colours to represent each image’s target class. ii. Load the MNIST dataset and split it into a training set and a test set (take the first 60,000 instances for training, and the remaining 10,000 for testing). Train a Random Forest classifier on the dataset and time how long it takes, then evaluate the resulting model on the test set. iii. Next, use PCA to reduce the dataset’s dimensionality to 174. Train a new Random Forest classifier on the reduced dataset and see how long it takes. Was training much faster? Next, evaluate the classifier on the test set. How does it compare to the previous classifier? iv. Summary/conclude your discovery and insights.

4 Structure Prepare a jupyter notebook for this assignment. The structure of the Jupyter notebook should alternate texts and python codes and cover topics listed the in specific task.

Context
Design Report for Number Filtering Program with GUI

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