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A wide collection of various Machine learning applications including sentiment analysis, handwriting classification, recommendation engine, color extraction, fashion recommender and identifier, Google API usage and many more!!!!
Applied Natural Language Feature Engineering and built a content based recommendation system using NLP Models (Bag of words and TF-IDF()TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.) in problem of recommending similar products on e-commerce websites by using Amazon product advertising API.
Applied K Nearest Neighbours Algorithm in recognizing handwritten digits (0 to 9) from the MNIST dataset. Developed a system from scratch which includes a machine to understand and classify the images of handwritten digits as 10 digits (0–9). Accuracy of the model was around 98%.
Applied K Means Clustering Unsupervised Algorithm in Segmenting an image into set ok K Dominant Colors. Extracted the K Dominant Colors from the image and then re-colorized it using those K Colors.
Using Bag of words model we find the ‘term frequency’, i.e. number of occurrences of each word in the dataset, along with Term Frequency-Inverse Document Frequency to find probabality of each word, followed by additive smoothing.
It contains a basic use caseof Natural language processing, and using data classifiers on the dataset. Support vector machines are usedto effectively classify and accuracy is checked using F1 score and ROC-AUC curve.
The above code and application produced are just some of the Machine Learning applications that I worked on, through these years, and I have selected some of the relevant and intersesting applications for this repository, to help others learn and understand the real world applications of machine learning, so as to aid them to get out of theoretical knowledge of books. These work are develoed throughout my college period, while studying machine learning and deep learning, with help of lots of books, blogs, youtube videos and many more, who have helped me learn. Some of them might be inspired from these teachings, and will have some changes and learnings of my own, while most are original work, produced after deep understanding of the underlying technique. I will appreciate anybody who is willing to contribute or learn can just fork the repository and May the training parameters forever be in your favour :)
Thank You!!