AI/ML Lab for Engineering and Science

Course Code
AIMLLab-Custom

Propel your machine learning skills to new heights for industry-grade applications in both research and real-world product environments. This intensive, hands-on, five-day certificate program is designed to hone your expertise based on the Institute's proven methodology for nurturing researchers' capabilities. Embark on this transformative journey and unlock your full potential in the realm of AI and machine learning.

  • Learners Any Level
  • Time Client definable
  • Duration 5 Days
  • Program Type Customizable Programs
  • Certificate Type Certificate Available
  • Format Blended
  • CEU/PDU Available
  • Fees Group Rate

Propel your machine learning skills to new heights for industry-grade applications in both research and real-world product environments. This intensive, hands-on, five-day certificate program is designed to hone your expertise based on the Institute's proven methodology for nurturing researchers' capabilities. Embark on this transformative journey and unlock your full potential in the realm of AI and machine learning.

AI/ML Lab for Engineering and Science

Program Experience

Delve into the foundations of machine learning algorithms while gaining a deep understanding of data preprocessing, model selection, and evaluation criteria. Through diverse use cases, we will guide you along the entire ML lifecycle, arming you with a potent toolkit to conquer industry challenges with confidence.

Understand practical, applied machine learning techniques that you can implement immediately. While we touch upon the statistical foundations of modern ML, our primary focus is on equipping you as a real-life industry practitioner in engineering and research domains.

This course is tailored for technically adept learners who wish to gain a comprehensive understanding of the entire AI/ML lifecycle, exploring the intricate trade-offs between commercial demands and academic pursuit of algorithm development. 

Benefits

Participants will advance their existing machine learning skills and be able to:

  • Understand industry applications of the end-to-end ML lifecycle

  • Complete the foundations of ML with analytical methods and statistical deep dives

  • Preprocess data to fit the needs of modern ML algorithms

  • Understand the entire ML lifecycle and its applications

  • Create robust modelling of supervised and unsupervised algorithms

  • Know which algorithm to select for various real-world scenarios

  • Approach deep learning with applied knowledge of neural networks

  • Leverage deep learning methods using modern tools like PyTorch

  • Know the differences in compute when using deep learning

  • Apply foundational large language models (LLMs) to current use cases

Topics

The course covers the entire machine learning lifecycle and the toolkit needed to create robust machine learning pipelines:

  • Data preprocessing techniques

  • Statistical foundations of ML

  • Exploratory Data Analysis

  • Python best-practices

  • Modern ML libraries

  • Model selection and hyperparameter tuning

  • Supervised ML algorithms

  • Unsupervised techniques

  • ML pipelines

  • Data bias and how to avoid it in modelling

  • Dimensionality reduction toolkit

  • Deep Learning with neural networks

  • PyTorch application of deep learning

  • Bias-Variance Trade-off

  • Natural Language Processing (NLP)

  • NLP and deep learning applied to generative AI (LLMs)

  • Leverage deep learning techniques such as FFNNs, RNNs, LSTMs

  • The modern ML lifecycle

  • MLOps and ML deployment to production

Who Should Attend

This program is designed for experienced professionals with a background in engineering, science, or related fields such as aerospace, chemistry, biology, electronics, finance, communications, or technology. It is ideal for those who want to integrate data science and machine learning into their work. Learners are expected to have a solid understanding of calculus, linear algebra, probability, statistics, and basic programming skills, including Python/R. The course provides a balanced combination of theoretical concepts and practical applications.

Instructors

Photo of Nicholas Beaudoin

Nicholas Beaudoin

Machine Learning, Generative AI