Fee: US $13140.00

Next set of dates would be announced soon


Fundamentals of Machine Learning and AI

  • What is Machine Learning?
  • What problems does it try to solve?
  • What are the main categories and fundamental concepts of Machine Learning systems?
  • The main steps in a typical Machine Learning project.
  • Learning by fitting a model to data.
  • Optimizing a cost function.
  • Handling, cleaning, and preparing data.
  • Selecting and engineering features.
  • Selecting a model and tuning hyperparameters using cross-validation.
  • The main challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance tradeoff).
  • Reducing the dimensionality of the training data to fight the curse of dimensionality.
  • The most common learning algorithms:
  • Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods.

Neural Networks and Deep Learning

  • What are neural nets? What are they good for?
  • Building and training neural nets using TensorFlow
  • The most important neural net architectures:
  • feedforward neural nets
  • convolutional nets
  • recurrent nets
  • long short-term memory (LSTM) nets
  • autoencoders
  • Techniques for training deep neural nets
  • Scaling neural networks for huge datasets
  • Reinforcement learning
  • High School Math: Brush up on probability and linear algebra with Khan Academy
  • Use the Cloud Genius Workstation, roll your own Jupyter notebook using Docker, or use a cloud service as your playground
  • Review the prerequisite Jupyter notebooks in the getting started segment

Getting Started

Before you start:

We don't expect you to be a math wiz or an expert python developer but we assume that you are naturally intelligent and have no prior knowledge of artificial intelligence or machine learning.

  • Math – We will use some notions of high school level math, probability/statistics, calculus, and linear algebra.
    You should be able to follow along if you learned these in the past as it won't be very advanced. It would be a great idea to visit the Khan Academy and brush up on your high school curriculum.
  • Python – We don't expect you to write Python code but will assume that you can read and understand basic Python.
    The official tutorial at Python.org is a good place to start.

Expected outcome:

Our program helps you build your career as a Machine Learning/AI practitioner. This means you need to practise a lot!

The more you put into it, the more you’ll get out of it.

We do not expect you to become an AI/ML researcher at the end of this program. You could pursue a PhD or something for that.

What we cover:

You will learn the Concepts, the Intuition, and the Tools you need to implement intelligent systems capable of learning from data using production ready open source implementations of modern algorithms. We will use Scikit-learn, Tensorflow and Keras running in a Jupyter notebook.

How do we operate:

Eight weeks of videoconference based face to face live sessions covering concepts with detailed real life examples:

  • Interactive hands-on exercises in Jupyter
  • Theory only when necessary to understand concepts
  • Begin on a set start date and run for 8 weeks
  • Twice a week three hours hands-on videoconference (Tuesdays + Thursdays)
  • 4pm - 7pm Pacific Daylight Time (https://time.is/Seattle)

No need to drive to a physical classroom

We record each and every session

Session recordings become available here for participants so you can rewind/repeat session recording soon after the live session ends so no worries if you miss a session. Instructors are available for help when you work in-between (before/after) our scheduled video conference sessions.

Recordings, content, and discussions are open for 180+ days after the program starts. That gives you plenty of time to work on your own even after the live sessions are finished.


 Cloud Genius is a highly rated advanced technical education provider licensed by the State of Washington, USA.