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Course Skill Level:


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, Python, & Linux skills who want to build ML algorithms with Scikit-Learn & Python

Who should attend & recommended skills

  • This course is geared for those who want to know the features of Scikit-Learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level.
  • Skill-level: Foundation-level Machine Learning Fundamentals skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Python programming: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You’ll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You’ll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the course then shifts to supervised learning algorithms. You’ll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You’ll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this course, you will have gained all the skills required to start programming machine learning algorithms.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning Fundamentals expert instructor, students will learn about and explore:
  • Scikit-learn uniform API and its application into any type of model
  • The difference between supervised and unsupervised models
  • The usage of machine learning through real-world examples
  • The importance of data representation
  • Gaining insights into the differences between supervised and unsupervised models
  • Exploring data using the Matplotlib library
  • Studying popular algorithms, such as k-means, Mean-Shift, and DBSCAN
  • Measuring model performance through different metrics
  • Implementing a confusion matrix using scikit-learn
  • Studying popular algorithms, such as Nave-Bayes, Decision Tree, and SVM
  • Performing error analysis to improve the performance of the model
  • Learning to build a comprehensive machine learning program

Course breakdown / modules

  • Introduction
  • Scikit-Learn
  • Data Representation
  • Data Preprocessing
  • Scikit-Learn API
  • Supervised and Unsupervised Learning

  • Introduction
  • Clustering
  • Exploring a Dataset: Wholesale Customers Dataset
  • Data Visualization
  • k-means Algorithm
  • Mean-Shift Algorithm
  • DBSCAN Algorithm
  • Evaluating the Performance of Clusters

  • Introduction
  • Model Validation and Testing
  • Evaluation Metrics
  • Error Analysis

  • Introduction
  • Exploring the Dataset
  • Naive Bayes Algorithm
  • Decision Tree Algorithm
  • Support Vector Machine Algorithm
  • Error Analysis

  • Introduction
  • Artificial Neural Networks
  • Applying an Artificial Neural Network
  • Performance Analysis

  • Introduction
  • Program Definition
  • Saving and Loading a Trained Model
  • Interacting with a Trained Model