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Unsupervised Learning with Python

  • Course Code: Artificial Intelligence - Unsupervised Learning with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Time Series Analysis with R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data. 
  • 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. 

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. 

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

Learn how to select the most suitable Python library to solve your problem 

Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them 

Delve into the applications of neural networks using real-world datasets 

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

Understand the basics and importance of clustering 

Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages 

Explore dimensionality reduction and its applications 

Use scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset 

Employ Keras to build autoencoder models for the CIFAR-10 dataset 

Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data 

Audience & Pre-Requisites 

This course is for readers want to Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT and Python Skills. 
  • Good foundational mathematics or logic skills 
  • For readers with existing programming skills. 

Course Agenda / Topics 

  1. Introduction to Clustering 

Introduction to Clustering 

Introduction 

Unsupervised Learning versus Supervised Learning 

Clustering 

Introduction to k-means Clustering 

Activity 1: Implementing k-means Clustering 

  1. Hierarchical Clustering 

Hierarchical Clustering 

Introduction 

Clustering Refresher 

The Organization of Hierarchy 

Introduction to Hierarchical Clustering 

Linkage 

Agglomerative versus Divisive Clustering 

k-means versus Hierarchical Clustering 

  1. Neighborhood Approaches and DBSCAN 

Neighborhood Approaches and DBSCAN 

Introduction 

Introduction to DBSCAN 

DBSCAN Versus k-means and Hierarchical Clustering 

  1. Dimension Reduction and PCA 

Dimension Reduction and PCA 

Introduction 

Overview of Dimensionality Reduction Techniques 

PCA 

  1. Autoencoders 

Autoencoders 

Introduction 

Fundamentals of Artificial Neural Networks 

Autoencoders 

  1. t-Distributed Stochastic Neighbor Embedding (t-SNE) 

t-Distributed Stochastic Neighbor Embedding (t-SNE) 

Introduction 

Stochastic Neighbor Embedding (SNE) 

t-Distributed SNE 

Interpreting t-SNE Plots 

  1. Topic Modeling 

Topic Modeling 

Introduction 

Cleaning Text Data 

Latent Dirichlet Allocation 

Non-Negative Matrix Factorization 

  1. Market Basket Analysis 

Market Basket Analysis 

Introduction 

Market Basket Analysis 

Characteristics of Transaction Data 

Apriori Algorithm 

Association Rules 

  1. Hotspot Analysis 

Hotspot Analysis 

Introduction 

Kernel Density Estimation 

Hotspot Analysis 

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