Introduction to Machine Learning

Course Description

Explore the conceptual framework of commonly used AI algorithms in scikit-learn* and how to solve machine learning problems using the Intel® Extension for Scikit-learn*.

Intel Extension for Scikit-learn:

Enhances the performance of scikit-learn, which is a widely used AI library for machine learning.
Provides acceleration on Intel® CPUs and GPUs for as much as three orders of magnitude on several dozen commonly used scikit-learn algorithms.
Uses a process called patching, which you can apply to commonly used AI algorithms with minimal code changes (usually two lines of code).

Modules

11 modules (Estimated time to complete: 21 hours)
11 lab exercises

Modules

Introduction to Machine Learning
Supervised Learning and K-Nearest Neighbor
Train Test Splits, Cross Validation & Linear Regression
Regularization and Gradient Descent
Logistic Regression and Classification Error Metrics
Support Vector Machines (SVM) and Kernels
Decision Trees
Bagging
Boosting and Stacking
Introduction to Unsupervised Learning and Clustering Methods
Dimensionality Reduction and Advanced Topics

Get the Assignments and Quizzes

Eleven lab exercises with slide presentations and quizzes are available as a separate download.

Request the Full Kit

Learning Objectives

After completing this course, students will be able to:

Describe the conceptual framework and application of commonly used scikit-learn algorithms across a variety of problem domains.
Accelerate algorithms for clustering, classification, regression, dimensionality reduction, and more.
Apply Intel Extension for Scikit-learn patching to accelerate commonly used scikit-learn algorithms.
Apply scikit-learn algorithms to solve specified problems described in each notebook.
Describe where to download and install the AI Tools.