Machine Learning

In this course, students will delve into the use of an iterative machine learning (ML) process pipeline to solve real-world business problems in a project-based learning environment. They will learn about each phase of the process pipeline, including demonstrations and presentations from the instructors. By the course’s end, students will have built, trained, evaluated, tuned, and deployed an ML model that addresses one of the given business problems: fraud detection, recommendation engines, or flight delays. This course is suitable for learners with minimal to no machine learning experience, although a basic knowledge of statistics would be advantageous.

• Course level: Intermediate

This course prepares you for a specialty certification exam in machine learning, certifying your ability to design, execute, deploy, and maintain machine learning solutions. You’ll understand the logistics of the exam and the mechanics of exam questions while exploring the exam’s technical domains. You’ll study vital services and key ideas relevant to the exam domains:

• Data Engineering
• Exploratory Data Analysis
• Modeling
• Machine Learning Implementation and Operations

Furthermore, you will master essential test-taking strategies and put them into practice by tackling several practice questions. Once you’ve honed your skills, you’ll have the opportunity to take a quiz to identify your strengths and weaknesses, guiding your focus in your pre-exam studies.

• Course level: Advanced

This one-day course introduces students to cloud-based deep-learning solutions. The training delves into the utility of deep learning and its various concepts. Students will also learn how to run their models on the cloud and gain a deeper understanding of deploying their deep learning models while designing intelligent systems based on deep learning.

• Course level: Intermediate

This course extends the DevOps practice prevalent in software development to build, train, and deploy machine learning models. The course underscores the importance of data, model, and code for successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork to navigate the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and act when model predictions in production start to drift from agreed-upon key performance indicators.

The Data Science Studio assists data scientists in preparing, building, training, deploying, and monitoring machine learning models swiftly. By bringing together a broad set of capabilities purpose-built for ML, the course aims to enhance productivity at every step of the ML lifecycle for experienced data scientists.

In this course, students will learn how to solve a real-world use case with Machine Learning and produce actionable results. It provides a walk-through of the stages of a typical data science process for Machine Learning, including analyzing and visualizing a dataset, preparing data, and feature engineering. It also covers the practical aspects of model building, training, tuning, and deployment. The course includes real-life use case examples, such as customer retention analysis to inform customer loyalty programs.