Machine Learning is having an impact on just about every industry. Data is everywhere, and organizations that can sift through their mountain of data for key insights using machine learning will emerge ahead of the competition. Machine Learning is at work “under the covers” in many applications that we interact with every day.
IBM is offering a free class for clients and practitioners to learn about Machine Learning. This full day session will introduce Machine Learning concepts via a presentation, followed by hands-on labs. The session will be instructed in person by IBM Data Science practitioners. The labs will provide a detailed overview of Machine Learning using open source technologies like Python, Apache Spark, XGBoost, scikit-learn, and Tensor Flow as well as IBM value-add capabilities such as one-click model deployment, a visual drag and drop predictive analytics tool, and a point and click data preparation tool. We will also cover IBM's Adversarial Robustness Toolbox an open source software library that supports both researchers and developers in defending deep neural networks against adversarial attacks making AI systems more secure.
WHO SHOULD GO:
Anyone interested in learning more about Machine Learning.
A working knowledge of Coding (Preferred Python), and a basic understanding of Data Science concepts is desirable, but not mandatory.
WHAT TO EXPECT: Expect to spend a full day of lecture and hands on exercises attacking real-world data challenges using Machine Learning. In 8 hours you will learn the essentials of Machine Learning and why it's important to your organization.
The hands-on labs will be performed using IBM’s fully managed Data Science Cloud platform (Watson Studio). Instructions to create a free Watson Studio account can be found at the link below;
The hands-on labs will be performed using IBM’s fully managed Data Science Cloud platform (Watson Studio). Instructions to create a free Watson Studio account are at the following link.
Watson Studio sign-up instructions
Watson Studio home page