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Course Skill Level:

Foundational to Intermediate

Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    SUMLPYL21E09

Who should attend & recommended skills:

Those with basic IT and programming skills

Who should attend & recommended skills

  • Those who want a machine to think for itself!
  • Skill-level: Foundation-level Supervised Machine Learning with Python skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Programming skills: Basic (1-2 years’ experience)

About this course

Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it’s crucial to know how a machine “learns” under the hood. This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you’ll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this course, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems.

Skills acquired & topics covered

  • Those who want a machine to think for itself!
  • Skill-level: Foundation-level Supervised Machine Learning with Python skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Programming skills: Basic (1-2 years’ experience)

Course breakdown / modules

  • Technical requirements
  • An example of supervised learning in action
  • Setting up the environment
  • Supervised learning
  • Hill climbing and loss functions
  • Hill climbing and descent
  • Model evaluation and data splitting

  • Technical requirements
  • Parametric models
  • Implementing linear regression from scratch
  • Logistic regression models
  • Implementing logistic regression from scratch
  • The pros and cons of parametric models

  • Technical requirements
  • The bias/variance trade-off
  • Introduction to non-parametric models and decision trees
  • Decision trees
  • Implementing a decision tree from scratch
  • Various clustering methods
  • Implementing KNNs from scratch
  • Non-parametric models – pros/cons

  • Technical requirements
  • Recommended systems and an introduction to collaborative filtering
  • Matrix factorization
  • Matrix factorization in Python
  • Content-based filtering
  • Neural networks and deep learning
  • Neural networks
  • Using transfer learning