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Machine Learning Fundamentals

  • Course Code: Artificial Intelligence - Machine Learning Fundamentals
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants 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.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning Fundamentals skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants 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. 
  • 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. 

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 gain all the skills required to start programming machine learning algorithms. 

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

  • Explore scikit-learn uniform API and its application into any type of model 
  • Understand the difference between supervised and unsupervised models 
  • Learn the usage of machine learning through real-world examples 

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

  • Understand the importance of data representation 
  • Gain insights into the differences between supervised and unsupervised models 
  • Explore data using the Matplotlib library 
  • Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN 
  • Measure model performance through different metrics 
  • Implement a confusion matrix using scikit-learn 
  • Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM 
  • Perform error analysis to improve the performance of the model 
  • Learn to build a comprehensive machine learning program 

Audience & Pre-Requisites 

This course is geared for attendees wants 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 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Python programming knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to Scikit-Learn 
  • Introduction to Scikit-Learn 
  • Introduction 
  • Scikit-Learn 
  • Data Representation 
  • Data Preprocessing 
  • Scikit-Learn API 
  • Supervised and Unsupervised Learning 
  1. Unsupervised Learning: Real-Life Applications 
  • Unsupervised Learning: Real-Life Applications 
  • Introduction 
  • Clustering 
  • Exploring a Dataset: Wholesale Customers Dataset 
  • Data Visualization 
  • k-means Algorithm 
  • Mean-Shift Algorithm 
  • DBSCAN Algorithm 
  • Evaluating the Performance of Clusters 
  1. Supervised Learning: Key Steps 
  • Supervised Learning: Key Steps 
  • Introduction 
  • Model Validation and Testing 
  • Evaluation Metrics 
  • Error Analysis 
  1. Supervised Learning Algorithms: Predict Annual Income 
  • Supervised Learning Algorithms: Predict Annual Income 
  • Introduction 
  • Exploring the Dataset 
  • Naïve Bayes Algorithm 
  • Decision Tree Algorithm 
  • Support Vector Machine Algorithm 
  • Error Analysis 
  1. Artificial Neural Networks: Predict Annual Income 
  • Artificial Neural Networks: Predict Annual Income 
  • Introduction 
  • Artificial Neural Networks 
  • Applying an Artificial Neural Network 
  • Performance Analysis 
  1. Building Your Own Program 
  • Building Your Own Program 
  • Introduction 
  • Program Definition 
  • Saving and Loading a Trained Model 
  • Interacting with a Trained Model 
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