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
- Some Python programming is helpful, but not required
- Math concepts such as linear algebra and calculus are helpful, but not required
LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years!
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 learn what machine learning is and how it works, then this course is for you. This course is targeted at a broad audience at an introductory level. By the end of this course you will understand the benefits of machine learning, how it works, and what you need to do next. If you are a software developer interested in developing machine learning models from the ground up, then my second course, Practical Machine Learning by Example in Python might be a better fit.
There are a number of machine learning examples demonstrated throughout the course. Code examples are available on github. You can run each examples using Google Colab. Colab is a free, cloud-based machine learning and data science platform that includes GPU support to reduce model training time. All you need is a modern web browser, there's no software installation is required!
July 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.
March 2020 course updates migrate all examples to Google Colab and Tensorflow 2. Tensorflow 2 is one of the most popular machine learning frameworks used today. No software installation is required.
April/May 2020 course updates streamline content, include Jupyter notebook lectures and assignment. Jupyter notebook is the preferred environment for machine learning development.
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
Madhu is a professional machine learning practitioner and data scientist. Madhu has three decades of interdisciplinary experience applying great technology for many different organizations, such as FINRA, Apple, Blue Cross/Blue Shield, Food & Drug Administration, and the US Department of Defense.
Over the years, Madhu has developed numerous innovative products and solutions at start ups and established companies. Examples include: machine learning solutions, Internet of Things (IoT) devices, big data systems, mobile medical applications, as well as enterprise applications and specialized hardware for space science, 3D graphics, and wireless communications.
Madhu has been awarded US and EU patents and has authored multiple books and training courses. Madhu has presented papers at technology conferences all over the world, including London, Munich, and Sydney, and many US locations. Madhu is also a private helicopter pilot and enjoys playing electric guitar.