Data Analytics with Python, SQL & Spark

San Francisco Oct 21-22

Curriculum

This workshop will introduce you to essential concepts and practices for building compelling analyses and dashboards on datasets of any size. You will learn how to:

  • Use Python and Pandas to select, group and summarize your data
  • Decide what data to keep and what to ignore
  • Create compelling visualizations using Seaborn and Matplotlib
  • Connect and retrieve data from a database using Python
  • Extend your analyses to relational databases using SQL
  • Perform aggregations and combinations using SQL
  • Include unstructured data sources in your analysis using Spark
  • Scale up your analyses to Gb of data using Spark on AWS
  • Combine Spark and SQL for maximum flexibility and power

Machine Learning with Python, Pandas & Scikit-Learn

San Francisco Nov 4-5

Curriculum

The workshop is meant to provide you with a solid base to build your machine learning skills. In particular you will learn to:

  • Recognize problems that can be solved with Machine Learning
  • Select the right technique (is it a classification problem? a regression? needs preprocessing?)
  • Load and manipulate data with Pandas
  • Visualize and explore data with Matplotlib and Bokeh
  • Build regression, classification and clustering models with Scikit-Learn
  • Evaluate model performance with Scikit-Learn
  • Build, train and serve a predictive model using Python, Flask and Heroku

Intro to Deep Learning with Python & Keras

San Francisco Nov 18-19

Curriculum

The workshop is meant to introduce you to the concepts of deep learning and provide a solid base to build deeper knowledge in the field. You will learn:

  • Fundamentals of deep learning theory
  • How to approach and solve a problem with deep learning
  • Build and train a deep fully connected model with Keras
  • Build and train a Convolutional Neural Net with Keras on a cloud GPU machine
  • Build and train a Recurrent Neural Net with Keras on a cloud GPU machine
  • Application to Image processing/Text processing

Advanced Deep Learning with Python & Tensorflow

San Francisco. Dec 2-3

Curriculum

The workshop is meant to expand your skills in deep learning by exposing you to the Tensorflow and to real world case studies. You will learn:

  • Review of fundamental deep learning architectures (Fully Connected, Convolutional, Recurrent)
  • Build and train a model with pure Tensorflow
  • Online training / continuous training
  • Custom architectures and loss functions
  • Review of famous architectures (Inception, Wavenet)
  • Setting up a machine for deep learning / serving a model

Unsupervised and reinforcement Learning with Python, Tensorflow & OpenAI

San Francisco. Dec 16-17

Curriculum

The workshop is meant to introduce you to unsupervised deep learning and reinforcement learning. You will learn:

  • Train neural networks to play video games using Deep Q-Learning
  • Reduce the dimensionality of your data using autoencoders
  • Improve the efficiency of your algorithms with generative adversarial networks
  • Train AI agents to interact in an environment using OpenAI Gym
  • Train a Word2Vec model to encode natural language

Zero to Deep Learning™ 3 weekends bundle

San francisco Nov 4-5, nov 18-19, dec 2-3

First weekend:
Intro to Machine Learning with Python & Scikit-Learn

  • Recognize problems that can be solved with Machine Learning
  • Select the right technique (is it a classification problem? a regression? needs preprocessing?)
  • Load and manipulate data with Pandas
  • Visualize and explore data with Matplotlib and Bokeh
  • Build regression, classification and clustering models with Scikit-Learn
  • Evaluate model performance with Scikit-Learn
  • Build, train and serve a predictive model using Python, Flask and Heroku

Second weekend:
Intro to Deep Learning with Python (Keras/Tensorflow)

  • Fundamentals of deep learning theory
  • How to approach and solve a problem with deep learning
  • Build and train a deep fully connected model with Keras
  • Build and train a Convolutional Neural Net with Keras on a cloud GPU machine
  • Build and train a Recurrent Neural Net with Keras on a cloud GPU machine
  • Application to Image processing/Text processing

Third weekend:
Advanced Deep Learning with Python & Tensorflow

  • Review of fundamental deep learning architectures (Fully Connected, Convolutional, Recurrent)
  • Build and train a model with Tensorflow / TFLearn
  • Build and train a model with pure Tensorflow
  • Online training / continous training
  • Custom architectures and loss functions
  • Review of famous architectures (Inception, Wavenet)
  • Setting up a machine for deep learning / serving a model

Analytics to Reinforcement Learning 5 weekends bundle

San francisco Oct 21-22, Nov 4-5, nov 18-19, dec 2-3, Dec 16-17

Curriculum

The course provides a comprehensive introduction to data science with deep dives in data analytics, machine learning, deep learning and reinforcement learning. It is meant to provide a solid base to build deeper knowledge in the field.

First weekend:
Intro to Data Analytics with Python, SQL, Spark and Seaborn

  • Use Python and Pandas to select, group and summarize your data
  • Decide what data to keep and what to ignore
  • Create compelling visualizations using Seaborn and Matplotlib
  • Connect and retrieve data from a database using Python
  • Extend your analyses to relational databases using SQL
  • Perform aggregations and combinations using SQL
  • Include unstructured data sources in your analysis using Spark
  • Scale up your analyses to Gygabytes of data using Spark on AWS
  • Combine Spark and SQL for maximum flexibility and power

Second weekend:
Intro to Machine Learning with Python & Scikit-Learn

  • Recognize problems that can be solved with Machine Learning
  • Select the right technique (is it a classification problem? a regression? needs preprocessing?)
  • Load and manipulate data with Pandas
  • Visualize and explore data with Matplotlib and Bokeh
  • Build regression, classification and clustering models with Scikit-Learn
  • Evaluate model performance with Scikit-Learn
  • Build, train and serve a predictive model using Python, Flask and Heroku

Third weekend:
Intro to Deep Learning with Python (Keras/Tensorflow)

  • Fundamentals of deep learning theory
  • How to approach and solve a problem with deep learning
  • Build and train a deep fully connected model with Keras
  • Build and train a Convolutional Neural Net with Keras on a cloud GPU machine
  • Build and train a Recurrent Neural Net with Keras on a cloud GPU machine
  • Application to Image processing/Text processing

Fourth weekend:
Advanced Deep Learning with Python & Tensorflow

  • Review of fundamental deep learning architectures (Fully Connected, Convolutional, Recurrent)
  • Build and train a model with pure Tensorflow
  • Online training / continous training
  • Custom architectures and loss functions
  • Review of famous architectures (Inception, Wavenet)
  • Setting up a machine for deep learning / serving a model

Fifth weekend:
Reinforcement Learning with Python, Tensorflow and OpenAI

  • Train neural networks to play video games using Deep Q-Learning
  • Reduce the dimensionality of your data using autoencoders
  • Improve the efficiency of your algorithms with generative adversarial networks
  • Train AI agents to interact in an environment using OpenAI Gym and Universe
  • Train a Word2Vec model to encode natural language