mdiby,github

Classification between good and bad loan requests based on loan data for year 2013-2014 from Lending Club (https://www.lendingclub.com/info/download-data.action) that'll help investors in deciding which loans they should invest

lopq

Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.

lstm-crypto-predictor

Predicting cryptocurrency price using RNN-LSTM networks

lstm-sentiment-analysis

Sentiment Analysis with LSTMs in Tensorflow

lstmrnnstockr

(1) LSTM-RNN stock prices (historical closing precies of S&P500) prediction using keras with tensorflow. (2) Experiments APIs on the network's hyper-parameters are provided through './mmodel/experiment.py'. (3) a website is built using this prediction model as engine with Flask and MySQL.

machine-learning-a-case-study-approach

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

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