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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:

Basic IT, Programming, and Python skills

Who should attend & recommended skills

  • Those who want to design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data.
  • Skill-level: Foundation-level Time Series Analysis with R skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Python: Basic to Intermediate (1-5 years’ experience)
  • Programming: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • How to select the most suitable Python library to solve your problem
  • Comparing k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them
  • The applications of neural networks using real-world datasets
  • The basics and importance of clustering
  • Building k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages
  • Dimensionality reduction and its applications
  • Using scikit-learn (sklearn) to implement and analyze principal component analysis (PCA)on the Iris dataset
  • Employing Keras to build autoencoder models for the CIFAR-10 dataset
  • Applying the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data

Course breakdown / modules

  • Introduction
  • Unsupervised Learning versus Supervised Learning
  • Clustering
  • Introduction to k-means Clustering
  • Activity 1: Implementing k-means Clustering

  • Introduction
  • Clustering Refresher
  • The Organization of Hierarchy
  • Introduction to Hierarchical Clustering
  • Linkage
  • Agglomerative versus Divisive Clustering
  • k-means versus Hierarchical Clustering

  • Introduction
  • Introduction to DBSCAN
  • DBSCAN Versus k-means and Hierarchical Clustering

  • Introduction
  • Overview of Dimensionality Reduction Techniques
  • PCA

  • Introduction
  • Fundamentals of Artificial Neural Networks
  • Autoencoders

  • Introduction
  • Stochastic Neighbor Embedding (SNE)
  • t-Distributed SNE
  • Interpreting t-SNE Plots

  • Introduction
  • Cleaning Text Data
  • Latent Dirichlet Allocation
  • Non-Negative Matrix Factorization

  • Introduction
  • Market Basket Analysis
  • Characteristics of Transaction Data
  • Apriori Algorithm
  • Association Rules

  • Introduction
  • Kernel Density Estimation
  • Hotspot Analysis