Certificate in Machine Learning

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.

4 Classes

36 Hours

Schedule

January 17 to March 13, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (17 Jan 2026 to 18 Jan 2026); Fri 10:00 AM-5:00 PM (13 Mar 2026)

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.

4 Classes

36 Hours

Schedule

March 14 to May 08, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (14 Mar 2026 to 15 Mar 2026); Fri 10:00 AM-5:00 PM (08 May 2026)

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 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.

4 Classes

36 Hours

Schedule

May 09 to July 03, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (09 May 2026 to 10 May 2026); Fri 10:00 AM-5:00 PM (03 Jul 2026)

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.

4 Classes

36 Hours

Schedule

July 04 to September 12, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (04 Jul 2026 to 05 Jul 2026); Sat 10:00 AM-5:00 PM (12 Sep 2026)

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.

3 Classes

36 Hours

Schedule

September 19 to November 13, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (19 Sep 2026 to 20 Sep 2026); Fri 10:00 AM-5:00 PM (13 Nov 2026)

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.

3 Classes

36 Hours

Schedule

November 14, 2026 to January 22, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (14 Nov 2026 to 15 Nov 2026); Fri 10:00 AM-5:00 PM (22 Jan 2027)

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 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.

3 Classes

36 Hours

Schedule

January 23 to March 19, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (23 Jan 2027 to 24 Jan 2027); Fri 10:00 AM-5:00 PM (19 Mar 2027)

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.

3 Classes

36 Hours

Schedule

March 20 to May 15, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (20 Mar 2027 to 21 Mar 2027); Sat 10:00 AM-5:00 PM (15 May 2027)

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.

3 Classes

36 Hours

Schedule

January 16 to March 12, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (16 Jan 2027 to 17 Jan 2027); Fri 10:00 AM-5:00 PM (12 Mar 2027)

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.

3 Classes

36 Hours

Schedule

March 13 to May 07, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (13 Mar 2027 to 14 Mar 2027); Fri 10:00 AM-5:00 PM (07 May 2027)

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 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.

3 Classes

36 Hours

Schedule

May 08 to July 09, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (08 May 2027 to 09 May 2027); Fri 10:00 AM-5:00 PM (09 Jul 2027)

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.

3 Classes

36 Hours

Schedule

July 10 to September 11, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (10 Jul 2027 to 11 Jul 2027); Sat 10:00 AM-5:00 PM (11 Sep 2027)