Machine Learning with Python, Pandas & Scikit-Learn
For people starting out with machine learning and looking to speed up their learning curve. This course provides a solid structure to organize your learning as well as code snippets and best practices. In this course you will learn to how to building, train and deploy machine learning models to predict continuous and discrete quantities.
Data Analytics with Python, SQL and Spark
For people who are getting started with data analytics and want to analyze small and large datasets. In this course you learn to process structured and unstructured data, extract meaningful insights and visualize them. The course is a great starting point for the more advanced courses in machine learning and deep learning.
Advanced Deep Learning with Python & Tensorflow
For people who are already familiar with the fundamentals of machine learning and deep learning. Building on the basic and intermediate courses you will explore more advanced topics. In this course you will learn to use Tensorflow to train supervised deep learning models and you will discuss advanced topics like custom losses, model serving and transfer learning. You will review case studies from public repositories and discuss industry applications of deep learning at scale.
Intro TO Deep Learning with Python & Keras
For people who already have a good understanding of machine learning and want quickly add deep learning to their toolbox. This course builds on top of the machine learning course providing examples and tools to use deep learning models on real world data. In this course you will learn about Fully Connected, Convolutional and Recurrent Neural networks and you will build models that work with images, text and numerical data
Reinforcement Learning with Python, Tensorflow & OpenAI
For people who are familiar with supervised deep learning and want to venture into unsupervised territory. In this course you will learn all about Q-Learning, Autoencoders and Adversarial Networks. You will train agents to play video games and apply unsupervised learning to a variety of problems.
Zero to Deep Learning™ 3 weekends bundle
3 weekends bundle that includes the Machine Learning and Deep Learning courses at a discounted rate. Homework is provided for students to continue progressing between one weekend and the next.
Analytics to Reinforcement Learning 5 weekends bundle
5 weekends bundle that includes the all the 5 courses above at a discounted rate. Homework is provided for students to continue progressing between one weekend and the next.
WHAT MAKES US DIFFERENT?
We focus on Machine Learning and Deep Learning with Python.
Our courses are self-contained and hands-on. In 2 days you get to the core of the matter and learn enough foundations to bypass the painful part of approaching a new subject. Your learning curve will go from months to days.
Whether you are considering moving a career move, or simply want to include predictive modeling in your work, our workshops give you the tools to do so.
In the last 2 years Python has become a de-facto standard in data science and it is widely adopted by most major companies. Reasons for this success include:
- large set of mature data science libraries => most needs covered
- worldwide community of enthusiasts => get help when you need it
- easy to learn, read and write => start contributing immediately
- supports both functional and object oriented coding => versatile and powerful
- full stack programming language => easier interaction between data scientists and software engineers
Keras is a high-level neural networks api and library that allows to simply build and train deep learning models using Tensorflow or Theano as backend. Written in Python it focuses on enabling fast experimentation. It recently became the preferred high level api for Tensorflow and it thus provides a great entry point to approach Tensorflow. Keras highlights:
- Allows for easy and fast prototyping
- Supports Fully connected, Convolutional and Recurrent..
- Supports arbitrary connectivity schemes
- Runs seamlessly on CPU and GPU
- Integrates very well with Tensorflow and Tensorboard
There are many open source Deep Learning libraries. Tensorflow is backed by Google and is quickly becoming one of the most used libraries in the fields. It has a large and growing community of users and it is versatile and easy to learn. Highlights include
- largest community of developers
- state of the art models and nodes
- high scalability, can be distributed on many GPUs
- production performance and deployment tools
- very versatile and powerful for distributed high performance computing beyond neural networks
Apache Spark has revolutionized how we build and deploy data pipelines for ETL, Visualization and Machine Learning. Reasons for this success include:
- Flexible enough to run SQL-style queries, machine learning algorithms, and everything in between
- Fast and scalable: efficient memory use => runs up to 100x faster than Hadoop
- Supports data exploration and production workflows => same code that works on a laptop can be deployed to cloud-based computing clusters
- Free and open-source