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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Basic IT and programming skills

Who should attend & recommended skills

  • This course is geared for those looking to cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms. Get started with machine learning for the first time using a step-by-step machine learning tutorial with exercises and activities that help build key skills as you progress at your own pace, on your own terms, and use your physical print copy to redeem free access to the online interactive edition.
  • Skill-level: Foundation-level Supervised Learning 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

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.

Skills acquired & topics covered

  • Getting to grips with the fundamental of supervised learning algorithms
  • Discovering how to use Python libraries for supervised learning
  • How to load a dataset in pandas for testing
  • Using different types of plots to visually represent the data
  • Distinguishing between regression and classification problems
  • Learning how to perform classification using K-NN and decision trees

Course breakdown / modules

  • Introduction
  • Python Packages and Modules
  • Data Quality Considerations

  • Introduction
  • Exploratory Data Analysis (EDA)
  • Summary Statistics and Central Values
  • Missing Values
  • Distribution of Values
  • Relationships within the Data

  • Introduction
  • Regression and Classification Problems
  • Linear Regression
  • Multiple Linear Regression

  • Introduction
  • Autoregression Models

  • Introduction
  • Ordinary Least Squares as a Classifier
  • Logistic Regression
  • Classification Using K-Nearest Neighbors
  • Classification Using Decision Trees
  • Artificial Neural Networks

  • Introduction
  • One-Hot Encoding
  • Overfitting and Underfitting
  • Bagging
  • Bootstrapping
  • Boosting
  • Stacking

  • Introduction
  • Importing the Modules and Preparing Our Dataset
  • Evaluation Metrics
  • Splitting a Dataset
  • Performance Improvement Tactics