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Supervised Machine Learning with Python

  • Course Code: Artificial Intelligence - Supervised Machine Learning with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 1 Days Audience: This course is geared for those want machine to think for itself!

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level Supervised Machine Learning with Python skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those want machine to think for itself! 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by Supervised Machine Learning with Python instructor, students will learn about and explore: 

  • Delve into supervised learning and grasp how a machine learns from data 
  • Implement popular machine learning algorithms from scratch, developing a deep understanding along the way 
  • Explore some of the most popular scientific and mathematical libraries in the Python language 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Crack how a machine learns a concept and generalize its understanding to new data 
  • Uncover the fundamental differences between parametric and non-parametric models 
  • Implement and grok several well-known supervised learning algorithms from scratch 
  • Work with models in domains such as ecommerce and marketing 
  • Expand your expertise and use various algorithms such as regression, decision trees, and clustering 
  • Build your own models capable of making predictions 
  • Delve into the most popular approaches in deep learning such as transfer learning and neural networks 

Audience & Pre-Requisites 

This course is for readers want to machine to think for itself! 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 
  • Good foundational mathematics or logic skills 
  • For readers with existing programming skills. 

Course Agenda / Topics 

  1. First Step Towards Supervised Learning 
  • First Step Towards Supervised Learning 
  • 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 
  1. Implementing Parametric Models 
  • Implementing Parametric Models 
  • Technical requirements 
  • Parametric models 
  • Implementing linear regression from scratch 
  • Logistic regression models 
  • Implementing logistic regression from scratch 
  • The pros and cons of parametric models 
  1. Working with Non-Parametric Models 
  • Working with Non-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 
  1. Advanced Topics in Supervised Machine Learning 
  • Advanced Topics in Supervised Machine Learning 
  • 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 
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