Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
I'm a Graduate student and came into the job market and found a university degree wasn't sufficient to get a good paying job. I aimed at hottest technology in the market Big Data but the word BigData... Read More
Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More
It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The dataset used contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universite Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.
As the dataset was created using a PCA, preprocessing of data is of little scope in this problem. The imbalance between classes is compensated using oversampling and undersampling. The logistic regression, random forest, support vector machine, k-means are used, within a cross-validation framework. Lastly the recall and accuracy are considered as metrics while choosing the best classifier. A buffer section on outlier detection is added at the end.
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
In this data science project, we will look at few examples where we can apply various time series forecasting techniques.
In this project, we will use traditional time series forecasting methods as well as modern deep learning methods for time series forecasting.
