JLewiste/machine_learning_engineer_nanodegree

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Created about 8 years ago
Updated almost 4 years ago

JLewiste/machine_learning_engineer_nanodegree

JLewiste

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Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

white_blood_cells_classification_via_convolutional_neural_network

Given images of white blood cells, classify them as MONONUCLEAR OR POLYNUCLEAR.

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recurrent_neural_network_lstm_via_tensorflow

A simple demo on how to use RNN (LSTM) model to classify MNIST datasets via Tensorflow.

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spotify_streamer_stage_1

Clone of the popular music streaming app (Spotify) !

kaggle_the_nature_conservancy_fisheries_monitoring

Can you detect and classify species of fish?

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iris_classification_via_keras_and_scikit-learn

Perform machine learning classification via Keras and hyper-parameter tuning via GridSearchCV

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kaggle_two_sigma_connect_rental_listing_inquiries

Finding the perfect place to call your new home should be more than browsing through endless listings. RentHop makes apartment search smarter by using data to sort rental listings by quality. But while looking for the perfect apartment is difficult enough, structuring and making sense of all available real estate data programmatically is even harder. Two Sigma and RentHop, a portfolio company of Two Sigma Ventures, invite Kagglers to unleash their creative engines to uncover business value in this unique recruiting competition.

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data-engineering-nanodegree

Solution to all projects of Udacity's Data Engineering Nanodegree: Data Modeling with Postgres & Cassandra, Data Warehouse with Redshift, Data Lake with Spark and Data Pipeline with Airflow.

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kaggle_instacart_market_basket_analysis

In this competition, Instacart is challenging the Kaggle community to use this anonymized data on customer orders over time to predict which previously purchased products will be in a user’s next order.

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