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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:

Those with basic IT and programming skills

Who should attend & recommended skills

  • Those who want to Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.
  • Skill-level: Foundation-level Machine Learning with scikit-learn skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Programming: Basic (1-2 years’ experience)

About this course

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This course is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This course teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this course, you will be confident in building your own machine learning models for accurate predictions.

Skills acquired & topics covered

  • Build your first machine learning model using scikit-learn
  • Train supervised and unsupervised models using popular techniques such as classification, regression and clustering
  • How scikit-learn can be applied to different types of machine learning problems
  • How to work with all scikit-learn’s machine learning algorithms
  • install and set up scikit-learn to build your first machine learning model
  • Employ Unsupervised Machine Learning Algorithms to cluster unlabeled data into groups
  • Perform classification and regression machine learning
  • Use an effective pipeline to build a machine learning project from scratch

Course breakdown / modules

  • A brief introduction to machine learning
  • What is scikit-learn?
  • Installing scikit-learn
  • Algorithms that you will learn to implement using scikit-learn

  • Technical requirements
  • Preparing a dataset for machine learning with scikit-learn
  • The k-NN algorithm
  • Implementing the k-NN algorithm using scikit-learn
  • Fine-tuning the parameters of the k-NN algorithm
  • Scaling for optimized performance

  • Technical requirements
  • Understanding logistic regression mathematically
  • Implementing logistic regression using scikit-learn
  • Fine-tuning the hyperparameters
  • Scaling the data
  • Interpreting the logistic regression model

  • Technical requirements
  • Naive Bayes algorithm
  • Support vector machines

  • Technical requirements
  • The inner mechanics of the linear regression algorithm
  • Implementing linear regression in scikit-learn
  • Model optimization

  • Technical requirements
  • Classification trees
  • Regression trees
  • Ensemble classifier

  • Technical requirements
  • The k-means algorithm
  • Implementing the k-means algorithm in scikit-learn
  • Feature engineering for optimization
  • Cluster visualization
  • Going from unsupervised to supervised learning

  • Technical requirements
  • Why is performance evaluation critical?
  • Performance evaluation for classification algorithms
  • Performance evaluation for regression algorithms
  • Performance evaluation for unsupervised algorithms