anika-tahsin-s / datacamp_project

DataCamp_Project

SL No.	Project	Description
1.	The Hottest Topics in Machine Learning	Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world where groundbreaking work is published. In this Project, a large collection of NIPS research papers from the past decade to discover the latest trends in machine learning is analyzed. The techniques used here to handle large amounts of data can be applied to other text datasets as well.
2.	Naïve Bees: Image Loading and Processing	In this Project, the Python image library Pillow to load and manipulate image data is used. Also shown the common transformations of images and how to build them into a pipeline.
3.	Naïve Bees: Predict Species from Images	This project is the second part of a series of projects that walk through working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision.
4.	Naïve Bees: Deep Learning with Images	In this project, a simple deep learning model that can automatically detect honey bees and bumble bees is built, then a pre-trained model is loaded for evaluation. Used libraries: keras, scikit-learn, scikit-image, and numpy, among other popular Python. This project is the third part of a series of projects that walk through working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision.
5.	Predicting Credit Card Approvals	In this project, an automatic credit card approval predictor using machine learning techniques is built, just like the real banks do.
6.	ASL Recognition with Deep Learning	In this project, a convolutional neural network will be trained to classify images of ASL letters. After loading, examining, and preprocessing the data, the network will be trained and tested its performance.
7.	Find Movie Similarity from Plot Summaries	In this project, NLP is used to find the degree of similarity between movies based on their plots available on IMDb and Wikipedia.
8.	Classify Song Genres from Audio Data	In this project, using a dataset comprised of songs of two music genres (Hip-Hop and Rock), a classifier is trained to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify).
9.	Reducing Traffic Mortality in the USA	In this project, it is shown that how can we find a good strategy for reducing traffic-related deaths.
10.	Give Life: Predict Blood Donations	In this project, we will be looking at the full model-building process: from inspecting the dataset to using the tpot library to automate a Machine Learning pipeline.
11.	Comparing Cosmetics by Ingredients	In this project, a ingredient lists for 1472 cosmetics on Sephora will be processed via word embedding, then visualize ingredient similarity using a machine learning method called t-SNE and an interactive visualization library called Bokeh.