## 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