Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shresth26/30-days-of-ml
Challenge to do Machine Learning projects for 30 days in a row
https://github.com/shresth26/30-days-of-ml
deep-learning machine-learning projects python statistics
Last synced: 15 days ago
JSON representation
Challenge to do Machine Learning projects for 30 days in a row
- Host: GitHub
- URL: https://github.com/shresth26/30-days-of-ml
- Owner: shresth26
- Created: 2020-04-03T16:52:44.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-05-02T15:39:49.000Z (over 4 years ago)
- Last Synced: 2024-02-23T10:28:20.952Z (10 months ago)
- Topics: deep-learning, machine-learning, projects, python, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 4.41 MB
- Stars: 10
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
100-Days-of-ML
Day 1 (02-04-2020) : MNIST GAN
* Today I studied the concept of GAN(Generative Adversarial Network).
* Implemented DCGAN(Deep Convolutional Generative Adversarial Network) on the MNIST dataset to generate handwritten digits.
* I Would love to explore the applications of GAN in the upcoming days.
* Link to the notebook. Click here.
References:
Deep Convolutional Generative Adversarial Network (Tensorflow doc)
What is Generative Adversarial Networks GAN?
Day 2 (03-04-2020) : Regularization and Optimization
* Studied various regularization techniques in order to handle overfitting.
* Completed Week 1 assignment of the Andrew Ng Deep Learning course.
* Explored various optimization algorithms such as Gradient descent with momentum, RMSprop and Adam.
* Completed Week 2 assignment of the same course.
* Course link : Click here
Day 3 (04-04-2020) : FACE-GAN
* Implemented DCGAN(Deep Convolutional Generative Adversarial Network) on the face dataset to generate human faces.
* The image generated was not upto the mark and would need some modification.
* The dataset is available Here
* Link to the notebook is available here
Day 4 (05-05-2020) : Structural Machine Learning
* Learnt about Machine Learning structural strategies used in the industry.
* Read about terms like Avoidable bias and variance on the dataset and about Bayes error.
* Read about Transfer Learning, Multi-task learning and end-to-end deep learning.
* Completed the course 3 of the deep learning series by Andrew Ng.
Day 5 (06-05-2020) : Clustering in Machine Learning
* Today I studied about the various clustering algorithms used in ML.
* The list includes : K-means, Mean-shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM) and Agglomerative Hierarchical Clustering.
* Analysed the pros and cons of each algorithm and the applications of each of them.
* Looking forward to implement them in the coming days.
* Relevant links on the topic. Link 1
Link 2
Link 3
Day 6 (07-04-2020) : Convolution
* Today I started course 4 of the Deep Learning Series by deeplearning.ai
* Week 1 turned out to be a quick recap of Convolution Neural Network (CNN).
* The topics covered todaytujhe bhula diya included padding, strides, convolutions and MaxPooling.
* The course is available Here.
Day 7 (08-04-2020) : Colorization
* Today I worked on the conversion of black images to images of color.
* Used OpenCV and Deep Learning to implement the program.
* The model used was pre-trained on the Caffe deep learning framework on ImageNet dataset.
* You can refer to the paper here and to the documentation here
* The results were plausible.
Day 8 (09-04-2020) : Background of CNN
* Today I dived deeper into the background of CNN.
* I wrote the code for it to understand what happens behind the scenes.
* It's always good to know about the theory and code of the pre-defined functions used directly by us.
* I implemented the model on the 'signs' dataset.
Day 9 (10-04-2020) : PyTorch
* Today I started learning about the basics of the PyTorch deep learning framework.
* Looked up the differences between Tensorflow and PyTorch.
* Implemented the framework on the University of California car dataset.
* Here's the notebook for it. 
Day 10 (11-04-2020) : ResNet and Inception
* Today I started Week 2 of the fourth course in the Deep learning specialization.
* Various architectures like ResNet, AlexNet and Inception were discussed in detail.
* Read about the effectiveness of 1x1 convolution.
* Would soon read the research papers on these architectures.
Day 11 (12-04-2020) : CIFAR10 Pytorch
* Today I implemented the CIFAR 10 dataset using Pytorch.
* The accuracy was not upto the mark since my primary focus was on understanding the framework.
* Would tweak the hyperparameters and increase the number of epochs to achieve higher accuracy.
* Would implement Pytorch on different datasets as well in the coming days.
* You can find the notebook here.
Day 12 (13-04-2020) : Math for Machine Learning
* Today I studied the required mathematics for machine learning.
* The topics included Linear Algebra, Multivariate Calculus, Probability and Calculus.
* The YouTube video for it is available here.
Day 13 (14-04-2020) : K-means Implementation
* Today I worked on the analysis of Mall Customer Segmentation data on Kaggle.
* The analysis involved the comparison between parameters like Customer age, salary and gender.
* K-means algorithm was used to form a cluster of customers on the basis of their shopping traits.
* The notebook is available here.
Day 14 (15-04-2020) : Time Series Analysis
* Today I read the theory of time series analysis and its applications.
* I was fascinated to know that time series analysis has a different approach as compared to conventional ML algorithms.
* The applications of time series really intrigued me and would implement it in the coming days.
* Relevant links.
7 Ways Time Series Forecasting Differs from Machine Learning
What Is Time Series Forecasting?
Day 15 (16-04-2020) : Basics of ML Libraries
* Completed the assignment 1 for SHALA2020 course.
* The assignment was an introduction to important libraries like Numpy, Pandas and Matplotlib.
* The notebook is available here
Day 16 (17-04-2020) : Introductory Statistics
* Revised statistic concepts assigned under the pre-work category on SHALA course.
* Consisted of topics like measuring central tendency, histograms and statistic fundamentals.
* Eventually gave a quiz on the related topic.
* Relevant links.
Day 17 (18-04-2020) : Plotting
* Completed assignment 2 of the IITB Shala course.
* Plotted and analysed data using Histograms, boxplots and pie chart.
* The notebook can be accessed here.
Day 18 (19-04-2020) : Airline Passenger Prediction
* Predicted the airline traffic for the future(3 years) using time series analysis using fbProphet.
* Used Markov Chain Monte Carlo method(MCMC) to generate forecasts.
* The model shows both the seasonality and trend in the data.
* You can check out the notebook here.
Day 19 (20-04-2020) : Milk Production Prediction
* Predicted the milk production for the future(3 years) using time series analysis using fbProphet.
* Used Markov Chain Monte Carlo method(MCMC) to generate forecasts.
* The model shows both the seasonality and trend in the data.
* You can check out the notebook here.
Day 20 (21-04-2020) : Graphs and Charts
* Read the theory of different types of graphs and their uses.
* Studied the differences between the types of graphs and charts.
* Learnt several visualization practices.
* Relevant links.
Essential Chart Types for Data Visualization
Day 21 (22-04-2020) : Graphs Implementation
* Completed assignment 2 of the IITB Shala course.
* The assignment consisted of different kind of charts and graphs that can be made using libraries like Pandas, Matplotlib and Seaborn.
* Explored various other visualization techniques as well.
* You can access the assignment here.
Day 22 (23-04-2020) : Intermediate Statistics
* Read a lot of intermediate statistics concepts.
* Topics included Maximum likelihood estimation, sufficient statistics, null hypothesis testing, t-test and Wilcoxon rank test.
* Would implement these concepts in the future.
Relevant Links:
Day 23 (24-04-2020) : Statistics Assignment
* Completed assignment 4 of the SHALA IITB course.
* With this the module 1 (Data Science) of the course has been completed.
* Computed the likelihood and log likelihood from samples that were drawn from an exponential distribution.
* Performed a two sample t-test from samples of unknown distributions and found the critical value.
* Notebook is available here.
Day 24 (25-04-2020) : Car Detection using YOLO
* Started week 3 of the CNN course by deeplearning.ai
* Read about object localization, object and landmark detection and non-max suppression.
* Studied the YOLO algorithm and implemented it in the week's assignment.
* Was able to complete the assignment and detect cars.
Day 25 (26-04-2020) : Neural Style Transfer
* Started Week 4 of the CNN deeplearning.ai course
* The topic for the week was art generation with neural style transfer.
* Understood the 2 types of cost functions i.e. Content cost function and Style cost function.
* Completed the assignment for art generation.
Day 26 (27-04-2020) : Face Recognition and Verification
* Read about one-shot learning and its application in face recognition.
* Understood the concepts of Siamese network and triplet loss.
* Implemented face recognition and verification in the assignment.
* With this completed the Course 4 of deeplearning.ai specialization.
Day 27 (28-04-2020) : Random Forest
* Read about Decision trees and Random Forest Regressor as well as Classifier.
* Gave a quiz on the topic and implemented the concepts in the SHALA ML assignment 1.
* Predicted the Attrition of the employees of a company using classifier.
Day 28 (29-04-2020) : Logistic Regression on Titanic dataset
* Made a classifier using Logistic Regression to predict the survival of a passenger in the Titanic dataset.
* Reached 79.3% accuracy, plotted the roc curve and calculated the roc_auc_score.
* Found out that Jack Dawson couldn't survive whereas Rose made it. Sigh!
* The notebook can be found here.
Day 29 (30-04-2020) : Titanic Dataset classifers
* Made several classifiers to predict passenger survival on the Titanic.
* Applied Logistic Regression, Decison Trees, Random Forest, Gradient Boosting and XGBoost algorithms.
* Corrected the anomalies of the logistic regression analyis performed the previous day.