In the current job market, it’s not enough to be a Python wizard or statistical genius. Top employers are looking for machine learning specialists who understand the business and ethical impact of their work. The Certificate in Machine Learning was created in collaboration with industry leaders, including major banks, ensuring that you graduate with the exact skills and real-world experience that will land you the job you want.
You’ll work on one main machine learning project from start to finish, giving you the chance to build upon your technical skill while gaining a holistic understanding of machine learning as it relates to current business applications. You’ll learn key skills that will allow you to:
- Build a business case to support the implementation of machine learning solutions in various industries
- Identify and assess the viability of different machine learning models using data, and explain your recommendations clearly to an employer
- Use project management principles to plan and execute a machine learning solution
- Use Python (and other languages) to create, implement and test machine learning algorithms in real-world scenarios
ML1000 Machine Learning in Business Context
You will learn the building blocks and tools that will empower you to take raw data sets and extract valuable insights and data visualizations that bring the data to life. You will be introduced to R programming and learn to manipulate data using various R libraries while working in RStudio and Weka. You will also be introduced to common algorithms and techniques for analyzing data and be introduced to the concepts, practices and tools that are used to implement machine learning. The course is designed to be practical and allow students to quickly apply their new skills to real world problems, with real datasets and using the CRISP-DM methodology.
ML1010 Applied Machine Learning and Lifecycle
You will cover the lifecycle of machine learning working with Python and R, including processing unstructured data, feature engineering, dimensionality reduction, model selection and optimization, performance evaluation, and model improvement. You will cover advanced algorithms for complex problems that require specialized methods. You will gain hands-on experience with advanced algorithms such as ensemble methods, sequence models, association rule mining, and neural networks. You will construct models using data from a variety of application domains. Time will also be allotted throughout the course to expose students to various ethical and public policy considerations of their work.
* successful completion of ML1000 is required to begin ML1010.
ML1020 Machine Learning at Scale
Having an understanding of the concepts of machine learning, you will examine and apply the fundamentals of working with extremely large data sets. First, you will parallelize and perform MapReduce operations using Hadoop and Spark. Second, you will take a look at the fundamentals of deep learning and working with TensorFlow/CNTK and Keras. You will focus on image and voice data and build deep neural network models. At the end of this module, you should be ready to take on a large-scale real-world project challenge.
* successful completion of ML1010 is required to begin ML1020.
ML1030 Machine Learning Capstone
In the machine learning capstone course, students address a business problem facing an organization by applying analytics models, methodologies, and tools learned in the program. The course will allow students to work on an end-to-end machine learning solution from problem formulation all the way to its deployment. As a result, students will create a data product that can be used to show their skill set to potential employers. The course will also provide an opportunity for students to work closely with business stakeholders, provides a networking opportunity, and exposes them to a real business setting.
* successful completion of ML1020 is required to begin ML1030.
We are updating our program policies. Please check again later.