We designed Machine Learning Deep Dive to do two things:
1. Provide you with expertise and confidence in building machine learning solutions to real problems.
2. Help you start your machine learning career with a significant portfolio of finished products. This foundation for your personal brand wil demonstrate your growth and possibilities as a professional. This is one of single most important ingredient for your future success.
We customize the curriculum and learning experience based on your needs
We accelerate students by focusing on where they are and where they need to go. This individualized support and mentorship means that we can help you take your next step whether that is launching your own company or building a deeply technical project.
We support you while pushing you forward.
We want to make sure the course fits your current level, while at the same time pushing you to the next level. It’s a delicate equilibrium that we achieve through (1) practical courses, (2) guided projects and (3) lectures that introduce you to cutting edge Machine Learning.
Block One
Learn the foundation of Machine Learning
The first block consists of classes by experienced professionals that will get you up to speed as a ML practitioner. You’ll learn and gain a deep understanding of the essential concepts while working on exciting, real-life tasks.
ADVANCED PYTHON and PYDATA STACK
Going deeper into Python and the Pydata stack A taste of linear algebra in numpy/pytorch Setting up a working environment Pandas
[ 32 hours ]
ENSURING EVERYTHING IS RIGHT
Data exploration and Testing
Enforcing constraints with engird
Automatic feature extraction with feature tools
[ 8 hours ]
FUNDAMENTALS OF MACHINE LEARNING
Understanding a selection of classical machine learning algorithms
Cross-validation, regularization
Data exploration
[ 16 hours ]
HOW TO WIN IN BUSINESS
How to get better performance in Kaggle competitions
How to communicate results in business
[ 16 hours ]
NEURAL NETWORKS FROM SCRATCH
Implementing a simple NN and backprop using Numpy
[ 8 hours ]
MODERN SQL
Retrieving data from relational databases in the 21st century
[ 8 hours ]
APPLIED ML USING SPARK
Data wrangling and ML models using Spark
[ 24 hours ]
ADVANCED PIPELINES
How to build data pipelines in sklearn
Introduction to testing in data pipelines and ML
[ 12 hours ]
ENSURING EVERYTHING IS RIGHT
Data exploration and Testing
Enforcing constraints with engird
Automatic feature extraction with feature tools
[ 8 hours ]
ADVANCED PIPELINES
How to build data pipelines in sklearn
Introduction to testing in data pipelines and ML
[ 8 hours ]
MODELS IN PRODUCTION
Palladium framework
Heuristics to decide when to retrain
Training on GPUs and going to production without them
[ 16 hours ]
MICROSERVICES
Building microservices
Building containers
flask
API design
[ 8 hours ]
Block Two
Build your first Machine Learning projects.
COMPUTER VISION
Going deep with Computer Vision
In this part, we begin our journey through deep learning products.
There are a lot of new, exciting applications and stateof- the-art models that we will be discussing in our lectures while you build a state of the art deep learning network that can locate different objects in a set of images.
Along the way, you’ll be practicing the following skills:
- Introduction to Pytorch Engineering skills: how to write clean code, how to set up a working repository Preprocessing and transforming images
- Deciding what architecture to use: convolutions, pooling, inception modules,…
- Finding the correct hyper-parameters and loss functions
- Transfer learning: leveraging pre-trained networks to boost your product Visualizing activations, weights Measuring and reporting performance
NATURAL LANGUAGE PROCESSING
Deep Learning NLP is currently a less mature field than other domains: whereas there are pretty effective techniques to deal with many problems in areas such as image processing, most of the challenges in NLP are still open to active research.
Precisely for that reason, it is an exciting field that also happens to be of immediate practical importance. We will explore this promising area and discuss a whole array of impressive applications as we build a smart chatbot.
Skills that you’ll practice as you build your model include:
- Representing language: embeddings, their geometry, how to avoid bias
- Recurrent neural network architectures: plain, GRUs, LSTMs Attention in RNNs
- Solving NPL problems: sentiment analysis, captioning, summarizing, translation, …
- How to build a chatbot that actually works; what is actually possible: the necessity of domain-specific solutions
- New frontiers in NLP
The second block consists of two projects that will provide you with the experience and confidence necessary to develop whole AI products from scratch.
We have chosen the two most foundational and sought-after domains in modern-day machine learning: Computer Vision and Natural Language Processing. Those two projects will make up the first part of your final portfolio.
Block 2
Invent and develop your own machine learning solution
In the third block, the training wheels fall off and you’ll work on your own project.
We’ll help you decide on an exciting application that showcases your expertise, and we will guide you through the process so that you end up with an impressive final product that you can show to the world, and that will finish up your personal portfolio.
CREATIVE PORTFOLIO PROJECT
We will assist you in finding a project that helps you brand yourself as a ML specialist and data scientist. You will apply many of the skills that you have developed during the previous parts to make sure your project is top notch.
As part of the project, we will also be discussing the following topics:
- Deciding on a personal project
- Debugging and troubleshooting your data product
- Deploying your model
- Career advice: how to present your portfolio, how to brand yourself, what to do next
Apply Now
Seize the moment and start your application today