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Applied Unsupervised Learning with Python

  • Course Code: Artificial Intelligence - Applied 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 Discover the skill-sets required to implement various approaches to Machine Learning with Python.

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 Discover the skill-sets required to implement various approaches to Machine Learning with Python. 
  • 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 about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this course, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This course starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this course, you will have learned the art of unsupervised learning for different real-world challenges. 

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

  • Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more 
  • Build your own neural network models using modern Python libraries 
  • Practical examples show you how to implement different machine learning and deep learning techniques 

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

  • Use cluster algorithms to identify and optimize natural groups of data 
  • Explore advanced non-linear and hierarchical clustering in action 
  • Soft label assignments for fuzzy c-means and Gaussian mixture models 
  • Detect anomalies through density estimation 
  • Perform principal component analysis using neural network models 
  • Create unsupervised models using GANs 

Audience & Pre-Requisites 

This course is for readers want to Discover the skill-sets required to implement various approaches to Machine Learning with Python. 

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. Getting Started with Unsupervised Learning 
  • Getting Started with Unsupervised Learning 
  • Technical requirements 
  • Why do we need machine learning? 
  • Types of machine learning algorithm 
  • Why Python for data science and machine learning? 
  1. Clustering Fundamentals 
  • Clustering Fundamentals 
  • Technical requirements 
  • Introduction to clustering 
  • K-means 
  • Analysis of the Breast Cancer Wisconsin dataset 
  • Evaluation metrics 
  • K-Nearest Neighbors 
  • Vector Quantization 
  1. Advanced Clustering 
  • Advanced Clustering 
  • Technical requirements
  • Spectral clustering 
  • Mean shift 
  • K-medoids 
  • Online clustering 
  1. Hierarchical Clustering in Action 
  • Hierarchical Clustering in Action 
  • Technical requirements 
  • Cluster hierarchies 
  • Agglomerative clustering 
  • Analyzing a dendrogram 
  • Cophenetic correlation as a performance metric 
  • Agglomerative clustering on the Water Treatment Plant dataset 
  • Connectivity constraints 
  1. Soft Clustering and Gaussian Mixture Models 
  • Soft Clustering and Gaussian Mixture Models 
  • Technical requirements 
  • Soft clustering 
  • Fuzzy c-means 
  • Gaussian mixture 
  1. Anomaly Detection 
  • Anomaly Detection 
  • Technical requirements 
  • Probability density functions 
  • Histograms 
  • Kernel density estimation (KDE) 
  • Anomaly detection 
  • One-class support vector machines 
  • Anomaly detection with Isolation Forests 
  1. Dimensionality Reduction and Component Analysis 
  • Dimensionality Reduction and Component Analysis 
  • Technical requirements 
  • Principal Component Analysis (PCA) 
  • Independent Component Analysis 
  • Topic modeling with Latent Dirichlet Allocation 
  1. Unsupervised Neural Network Models 
  • Unsupervised Neural Network Models 
  • Technical requirements 
  • Autoencoders 
  • Hebbian-based principal component analysis 
  • Unsupervised deep belief networks 
  1. Generative Adversarial Networks and SOMs 
  • Generative Adversarial Networks and SOMs 
  • Technical requirements 
  • Generative adversarial networks 
  • Self-organizing maps 
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