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Supervised Learning

  • Course Code: Artificial Intelligence - Supervised Learning
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Supervised Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms 
  • 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. 

You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You’ll learn from real examples that lead to real results. Throughout The Supervised Learning Workshop, you’ll take an engaging step-by-step approach to understand supervised learning. You won’t have to sit through any unnecessary theory. If you’re short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It’s your choice. Learning on your terms, you’ll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you’ll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You’ll even earn a secure credential that you can share and verify online upon completion. It’s a premium learning experience that’s included with your printed copy. To redeem, follow the instructions located at the start of your course. Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You’ll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you’ll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. 

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

  • Ideal for those getting started with machine learning for the first time 
  • A step-by-step machine learning tutorial with exercises and activities that help build key skills 
  • Structured to let you progress at your own pace, on your own terms 
  • Use your physical print copy to redeem free access to the online interactive edition 

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

  • Get to grips with the fundamental of supervised learning algorithms 
  • Discover how to use Python libraries for supervised learning 
  • Learn how to load a dataset in pandas for testing 
  • Use different types of plots to visually represent the data 
  • Distinguish between regression and classification problems 
  • Learn how to perform classification using K-NN and decision trees 

Audience & Pre-Requisites 

This course is for readers want to Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms 

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 

  • Fundamentals of Supervised Learning Algorithms 
  • Fundamentals of Supervised Learning Algorithms 
  • Introduction 
  • Python Packages and Modules 
  • Data Quality Considerations 
  1. Exploratory Data Analysis and Visualization 
  • Introduction 
  • Exploratory Data Analysis (EDA) 
  • Summary Statistics and Central Values 
  • Missing Values 
  • Distribution of Values 
  • Relationships within the Data 
  1. Linear Regression 
  • Introduction 
  • Regression and Classification Problems 
  • Linear Regression 
  • Multiple Linear Regression 
  1. Autoregression 
  • Introduction 
  • Autoregression Models 
  1. Classification Techniques 
  • Introduction 
  • Ordinary Least Squares as a Classifier 
  • Logistic Regression 
  • Classification Using K-Nearest Neighbors 
  • Classification Using Decision Trees 
  • Artificial Neural Networks 
  1. Ensemble Modeling 
  • Introduction 
  • One-Hot Encoding 
  • Overfitting and Underfitting 
  • Bagging 
  • Bootstrapping 
  • Boosting 
  • Stacking 
  1. Model Evaluation 
  • Introduction 
  • Importing the Modules and Preparing Our Dataset 
  • Evaluation Metrics 
  • Splitting a Dataset 
  • Performance Improvement Tactics 
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