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Machine Learning with scikit-learn, 2 Days

  • Course Code: Artificial Intelligence - Machine Learning with scikit-learn, 2 Days
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning with scikit-learn skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. 
  • 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. 

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. 

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

  • Build your first machine learning model using scikit-learn 
  • Train supervised and unsupervised models using popular techniques such as classification, regression and clustering 
  • Understand how scikit-learn can be applied to different types of machine learning problems 

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

  • Learn 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 unlabelled data into groups 
  • Perform classification and regression machine learning 
  • Use an effective pipeline to build a machine learning project from scratch 

Audience & Pre-Requisites 

This course is for readers want to Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. 

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. Introducing Machine Learning with scikit-learn 
  • Introducing Machine Learning with scikit-learn 
  • A brief introduction to machine learning 
  • What is scikit-learn? 
  • Installing scikit-learn 
  • Algorithms that you will learn to implement using scikit-learn 
  1. Predicting Categories with K-Nearest Neighbors 
  • Predicting Categories with K-Nearest Neighbors 
  • 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 
  1. Predicting Categories with Logistic Regression 
  • Predicting Categories with Logistic Regression 
  • Technical requirements 
  • Understanding logistic regression mathematically  
  • Implementing logistic regression using scikit-learn 
  • Fine-tuning the hyperparameters 
  • Scaling the data 
  • Interpreting the logistic regression model 
  1. Predicting Categories with Naive Bayes and SVMs 
  • Predicting Categories with Naive Bayes and SVMs 
  • Technical requirements 
  • The Naive Bayes algorithm  
  • Support vector machines 
  1. Predicting Numeric Outcomes with Linear Regression 
  • Predicting Numeric Outcomes with Linear Regression 
  • Technical requirements 
  • The inner mechanics of the linear regression algorithm 
  • Implementing linear regression in scikit-learn 
  • Model optimization  
  1. Classification and Regression with Trees 
  • Classification and Regression with Trees 
  • Technical requirements 
  • Classification trees 
  • Regression trees 
  • Ensemble classifier 
  1. Clustering Data with Unsupervised Machine Learning 
  • Clustering Data with Unsupervised Machine Learning 
  • 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 
  1. Performance Evaluation Methods 
  • Performance Evaluation Methods 
  • Technical requirements 
  • Why is performance evaluation critical? 
  • Performance evaluation for classification algorithms 
  • Performance evaluation for regression algorithms 
  • Performance evaluation for unsupervised algorithms 
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