Categories
Machine Learning

Hello, i need someone who’s proficient in ml (python) and the libraries:”gzip. numpy, sklearn, matplotlib and others” for a

Hello, i need someone who’s proficient in ML (Python) and the libraries:”gzip. numpy, sklearn, matplotlib and others” for a
homework involving: text mining, MSE, recommender system and category system, cosine similarity, ridge regression model
There is 2 parts: part-1) hw4 and part-2) assignment1 (only CSE158)
Book,chapters and assignment prompt provided. You have all the deliverables in the prompts, please provide comments

Categories
Machine Learning

You have all the deliverables in the prompts, please provide comments

Hello, i need someone who’s proficient in ML (Python) and the libraries:”gzip. numpy, sklearn, matplotlib and others” for a
homework involving: text mining, MSE, recommender system and category system, cosine similarity, ridge regression model
There is a file: hw4
Book,chapters and assignment prompt provided. You have all the deliverables in the prompts, please provide comments

Categories
Machine Learning

Please use the fashion mnist dataset in ‘neun_part_i code’ to implement the following network along with batch normalization and dropout layers

Please use the Fashion MNIST dataset in ‘NeuN_Part_I code’ to implement the following network along with batch normalization and dropout layers

Categories
Machine Learning

How would you handle them?

This assignment contains 3 problems
Problem 1 – MNIST Data Set (10 points)
Use the MNIST dataset to classify the type of apparel using an artificial nueral network.
https://www.kaggle.com/zalando-research/fashionmnist
You may use the library of your choosing
We went over this is class, so this is an easy one to get you started.
Grading criteria: Your results on the test data should exceed 0.80 accuracy and you have some explaination about the model.
Problem 2 – Poker Hand Classification (30 points)
Use the Pokerhand dataset at https://archive.ics.uci.edu/ml/machine-learning-databases/poker/
Pay attention to class distribution.Do you need to do anything to balance the data?
How does it affect ANNs if the training data is ordered?
I mentioned early stopping in class but didn’t go into detail. Implement early stopping in your modelThis article provides an example of early stopping with Keras and Tensorflow
https://towardsdatascience.com/a-practical-introduction-to-early-stopping-in-machine-learning-550ac88bc8fd
Provide a visualization of training and cross validation loss at each epoch
Provide a confusion matrix and F1 score for the test data
The output of the model will be probabilities of each class. I like
to use np.argmax to get the most probable class, but you may use other
techniques.
Problem 3 – Parkinson’s Telemonitoring (30 points)
In class, we focused on artificial nueral networks for classification purposes
Use the data set at https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/telemonitoring/The goal is to predict the ‘motor_UPDRS’
Remove the ‘total_UPDRS’ column. The goal of the data set author is
to predict both and ‘total_UPDRS’ but I don’t want to have two predicted
variables. I also don’t want to use it as a predictor because ‘total_UPDRS’ will be too colinear with ‘motor_UPDRS’
In this example, we want to use a regression ANN
Use a 80/20 train test split for your model
I acknowledge that you can solve this without an ANN, but please use a regression ANNMake sure you look at fields that are numbers but are not truly ordinal. How would you handle them?
This article provides an overview of regression ANN using Tensorflow. https://towardsdatascience.com/regression-based-neural-networks-with-tensorflow-v2-0-predicting-average-daily-rates-e20fffa7ac9a

Categories
Machine Learning

Provide a visualization of training and cross validation loss at each epoch

This assignment contains 3 problems Problem 1 – MNIST Data Set (10 points)
Use the MNIST dataset to classify the type of apparel using an artificial nueral network.
https://www.kaggle.com/zalando-research/fashionmnist
You may use the library of your choosing
We went over this is class, so this is an easy one to get you started.
Grading criteria: Your results on the test data should exceed 0.80 accuracy and you have some explaination about the model.
Problem 2 – Poker Hand Classification (30 points)
Use the Pokerhand dataset at https://archive.ics.uci.edu/ml/machine-learning-databases/poker/
Pay attention to class distribution.Do you need to do anything to balance the data? How does it affect ANNs if the training data is ordered?
I mentioned early stopping in class but didn’t go into detail. Implement early stopping in your modelThis article provides an example of early stopping with Keras and Tensorflow https://towardsdatascience.com/a-practical-introduction-to-early-stopping-in-machine-learning-550ac88bc8fd
Provide a visualization of training and cross validation loss at each epoch
Provide a confusion matrix and F1 score for the test data
The output of the model will be probabilities of each class. I like
to use np.argmax to get the most probable class, but you may use other
techniques.
Problem 3 – Parkinson’s Telemonitoring (30 points)
In class, we focused on artificial nueral networks for classification purposes
Use the data set at https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/telemonitoring/The goal is to predict the ‘motor_UPDRS’ Remove the ‘total_UPDRS’ column. The goal of the data set author is
to predict both and ‘total_UPDRS’ but I don’t want to have two predicted
variables. I also don’t want to use it as a predictor because ‘total_UPDRS’ will be too colinear with ‘motor_UPDRS’ In this example, we want to use a regression ANN
Use a 80/20 train test split for your model
I acknowledge that you can solve this without an ANN, but please use a regression ANNMake sure you look at fields that are numbers but are not truly ordinal. How would you handle them?
This article provides an overview of regression ANN using Tensorflow. https://towardsdatascience.com/regression-based-neural-networks-with-tensorflow-v2-0-predicting-average-daily-rates-e20fffa7ac9a

Categories
Machine Learning

Think about a situation where feature extraction might be required.

Using the attached file Feature_Selection.ipynb as the source, create an ipynb file
Think about a situation where feature extraction might be required. Share it through markdown cell(s) in your .ipynb notebook.
Starting from the auto_imports1.csv file and beginning at the point where you have built the df2 dataframe, use feature extraction techniques from the three main methods:
1. Filter methods
2. Wrapper methods
3. Embedded methods
to select and extract a subset of features.
Report your findings for each of the techniques you use, and compare them with each other.
Then, use PCA to decrease the dimensionality of the df2 dataframe, and compre PCA with feature extraction.

Categories
Machine Learning

Please use the fashion mnist dataset in ‘neun_part_i code’ to implement the following network along with batch normalization and dropout layers

Please use the Fashion MNIST dataset in ‘NeuN_Part_I code’ to implement the following network along with batch normalization and dropout layers

Categories
Machine Learning

Be with illustrations on analysis and design

I want to talk about the analysis chapter in the same attached file
And also a chapter talking about the design on the same attached file
Be with illustrations on analysis and design

Categories
Machine Learning

Please use the fashion mnist dataset in ‘neun_part_i code’ to implement the following network along with batch normalization and dropout layers

Please use the Fashion MNIST dataset in ‘NeuN_Part_I code’ to implement the following network along with batch normalization and dropout layers

Categories
Machine Learning

Share it through markdown cell(s) in your .ipynb notebook.

Using the attached file Feature_Selection.ipynb as the source, create an ipynb file
Think about a situation where feature extraction might be required. Share it through markdown cell(s) in your .ipynb notebook.
Starting from the auto_imports1.csv file and beginning at the point where you have built the df2 dataframe, use feature extraction techniques from the three main methods:
1. Filter methods
2. Wrapper methods
3. Embedded methods
to select and extract a subset of features.
Report your findings for each of the techniques you use, and compare them with each other.
Then, use PCA to decrease the dimensionality of the df2 dataframe, and compre PCA with feature extraction.