Practical Introduction to Machine Learning with Python

What you'll learn
Fundamentals of Artificial Intelligence (AI) and Machine Learning
Practical business applications of machine learning
Classification, regression, clustering, anomaly detection
How machines learn from data
Supervised, unsupervised, reinforcement, and transfer learning
How to identify problems suitable for machine learning
How to collect and prepare data suitable for training and testing machine learning models
Different types of machine learning models and how to choose among them
Machine learning development and production deployment process
How to train models using GPU instances in the cloud

Requirements
Some Python programming is helpful, but not required
Math concepts such as linear algebra and calculus are helpful, but not required

Description
Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Machine learning is an exciting and rapidly growing field full of opportunities. In fact, most organizations can not find enough AI and ML talent today.

If you want to start your journey towards becoming a machine learning practitioner or data scientist, then this course is for you. There's more to a successful ML project that just creating models and writing code. Identifying suitable problems, collecting, preparing and curating data sets, validating results, and maintaining quality over time are just as important as writing code. These challenges require a variety of skills, many of which are not super technical.

Whether you're a manager, business analyst, software developer, or someone looking to change careers, there's a place for you in a machine learning project. This course is aimed at giving you the knowledge you need to be productive in a changing economy where machines are climbing the corporate ladder.

There are a number of machine learning examples demonstrated throughout the course. Code examples are available on github. You have the option of hands-on experimentation with these examples on your local machine or Google Colab. Colab is a free, cloud-based machine learning and data science platform that includes GPU support to reduce model training time. Alternatively, some students are happy just watching the examples run and learning from the videos. It's completely up to you.

This is an introductory, thought based course. The course covers concepts that many might not have been exposed to before. Some of it might seem confusing in places, but that’s completely normal. Machine learning is quite different from conventional, imperative software. By the end of this course you will understand the benefits of machine learning, how it works, and what you need to do next.

IJuly 2019 course updates include lectures and examples of self-supervised learning. Self-supervised learning is an exciting technique where machines learn from data without the need for expensive human labels. It works by predicting what happens next or what's missing in a data set. Self-supervised learning is partly inspired by early childhood learning and yields impressive results. You will have an opportunity to experiment with self-supervised learning to fully understand how it works and the problems it can solve.

August 2019 course updates include a step by step demo of how to load data into Google Colab using two different methods. Google Colab is a powerful machine learning environment with free GPU support. You can load your own data into Colab for training and testing.

Who this course is for?
IT managers, business analysts, software architects, and developers interested in a quick start into the exciting and rapidly growing field of machine learning.
Business analysts or non-technical people who want to leverage their skills to add value in machine learning development project
Anyone wanting to learn where they can be productive in a changing economy where machines are climbing the corporate ladder

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