Machine-Learning

Task 1

• Matrix mult

• Deadline: Feb 19, 2020
• Uploading date: Mar 4, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/FirstTask.ipynb

In this task I created a program that multiplies matrices.

• Pure way in python: 1.3647644000000128
• Using Numpy: 0.0006633000000419997
• Own threading way: 0.10854268074035645

Task 2

• Linear regression

• Deadline: Feb 26, 2020
• Uploading date: Mar 31, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/SecondTask.ipynb

In this task I created a program that work with burrito.csv and real_estate.tsv. I completed task linear regression

Data:

• burrito.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/burrito.csv
• real_estate.tsv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/real_estate.tsv

Individual task 1

• Linear regression

• Deadline: Mar 11, 2020
• Uploading date: Mar 11, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/Induvidual.ipynb

In this task I worked with dataset House Sales in King County, USA. This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.

Note: Due to a bad internet connection, I asked Yaroslav Andreev to add task. Therefore, there are two contributors in the Induvidual.ipynb

Task 3

• Logistic regression

• Deadline: Mar 25, 2020
• Uploading date: Mar 31, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/ThirdTask.ipynb

In this task I created a program that work with sats.csv and tests.csv. I completed task logistic regression.

Data:

• sats.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/sats.csv
• tests.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/tests.csv

Task 4

• Neural networks

• Deadline: Apr 1, 2020
• Uploading date: Apr 2, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/FourthTask.ipynb

In this task I created a program that work with sats.csv and tests.csv. I completed task neural networks.

Data:

• sats.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/sats.csv
• tests.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/tests.csv

Individual task 2

• Binary logistic regression

• Deadline: Apr 8, 2020
• Uploading date: Apr 8, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/IT2_Iris.ipynb

In this task I worked with dataset Iris Species. The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository. It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

Data:

• Iris.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/Iris.csv
• IT2.jpg https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/IT2.jpg
• setosa.jpg https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/setosa.jpg
• versicolor.jpg https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/versicolor.jpg
• virginica.jpg https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/virginica.jpg

Task 5

• Softmax

• Deadline: Apr 8, 2020
• Uploading date: Apr 19, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/FifthTask.ipynb

In this task I worked with dataset Iris Species. I've added to my previous task Softmax function

Data:

• Iris.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/Iris.csv

Task 6

• Regularization & optimization

• Learning rate decay
• Deadline: Apr 15, 2020
• Uploading date: Apr 20, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/SixthTask.ipynb

To achieve better performance of your neural network, it is necessary to find the optimal value of learning rate that is not too large and not too small. There are several approaches to find the best learning rate. So in this task i will show work of NN with Learning Rate Decay and Learning Rate

Data:

• Iris.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/Iris.csv

Task 7

• Text classification

• Deadline: Apr 29, 2020
• Uploading date: Apr 25, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/SeventhTask.ipynb

In this task I am working with 20 Newsgroups dataset which has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. I have done all the requirements for the task:

• accuracy_score
• classification_report
• confusion_matrix

Task 8

• K-means clustering

• Deadline: May 13, 2020
• Uploading date: May 03, 2020
• Link: https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/EighthTask.ipynb

In this task I worked with dataset Iris Species and realized K-means Clustering.

Data:

• Iris.csv https://github.com/AnastasiaChernikova/Machine-Learning/blob/master/Iris.csv

Individual task 3

• Neural networks for multiclass classification

• Deadline: May 13, 2020
• Uploading date:
• Link: <...>

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Data: