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amazon-dsstne

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

apartment-finder

A Slack bot that helps you find an apartment.

applied-data-science-capstone

business-statistics-and-analysis-specialization

Mastered essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. Explored probability concepts, including measuring and modeling uncertainty, and various data distributions, along with the Linear Regression Model, to analyze and inform business decisions.

cloudml-samples

Cloud ML Engine is now a part of AI Platform

code-snippets

Small Google Cloud Platform examples and code snippets.

convolutional-neural-networks

Build convolutional neural networks and apply it to image data. Applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

dailydatasci.github.io

data-analysis-with-python

Used Pandas, Numpy and Scipy libraries to work with a sample dataset. Open-source library, load, manipulate, analyze, and visualize datasets. Use machine learning algorithms to build smart models and make cool predictions.

data-science-methodology

Understanding of the different stages that constitute the data science methodology, which is instrumental to solving any data science problem.

data-visualization-with-python

Used Python Libraries, mainly Matplotlib and Seaborn to visualize line plots, scatter plots, bubble plots, area plots, histograms, and bar charts. Also, used Folium library to visualize geospatial data and to create choropleth maps.

data-visualization-with-tableau-specialization

Examined, navigated, and learned to use the various features of Tableau. Assessed the quality of the data and perform exploratory analysis. Created and designed visualizations and dashboards for your intended audience. Combined the data to and follow the best practices to present your story.

databases-and-sql-for-data-science

Ipython, Cloud Databases, Relational Database Management System (RDBMS), SQL

dataquest.io

Being a Data Scientist

deep-learning.ai-specialization

Developed a profound knowledge of the hottest AI algorithms, mastered deep learning from its foundations (neural networks) to its industry applications (Computer Vision, Natural Language Processing, Speech Recognition, etc.).

end-to-end-machine-learning-with-tensorflow-on-gcp

Review the steps of deploying machine learning in a production environment. Explores a large dataset using Datalab and BigQuery. Use Pandas in Datalab and sample a dataset for local development. Develop a machine learning model in TensorFlow. Preprocess data at scale for machine learning

factbook.sql

World Factbook Country Profiles in SQL (Incl. factbook.db - Single-File SQLite Distro) - Free Open Public Domain Data

gitignore

A collection of useful .gitignore templates

hn

Hacker News: Crunching the Numbers

ibm-data-science-professional-specialization

Data Science & Machine Learning. Mastered Data Science, Python & SQL, Analyzed & Visualized Data, Built Machine Learning Models.

improving-deep-neural-networks-hyperparameter-tuning-regularization-and-optimization

Use industry best-practices for building deep learning applications. Initialization of L2 and dropout regularization, Batch normalization, gradient checking. Implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. Use best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance. Implement a neural network in TensorFlow.

machine-learning-with-python

Used Python, mainly Scikit-learn and Scipy, to ML algorithms such as decision trees, logistic regression, k-means, KNN, DBSCCAN, SVM and hierarchical clustering.

machine-learning-with-tensorflow-on-google-cloud-platform-specialization

Distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Converted raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Learned how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. Experimented with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

neural-networks-and-deep-learning

Understand trends driving Deep Learning. Build, train and apply fully connected deep neural networks. Know how to implement efficient (vectorized) neural networks. Understand the key parameters in a neural network's architecture.

open-source-tools-for-data-science

Understanding of how popular data science tools such as the Jupyter Notebook, RStudio, Zeppelin and Watson Studio are used

presto

Official home of Presto, the distributed SQL query engine for big data

python-for-data-science

Knowledge to work with data and develop applications for data science. Python knowledge to work with Python libraries.

qwiklabs-training-content

markup to create labs for courses from the Google Cloud training catalog.

sequence-models

Build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. Apply sequence models to natural language problems, including text synthesis. Apply sequence models to audio applications, including speech recognition and music synthesis.

solutions

Solutions for projects.