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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:

Python experienced developers seeking to implement machine learning

Who should attend & recommended skills

  • This course is geared for those who want to discover the skill sets required to implement various approaches to Machine Learning with Python.
  • 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 to Intermediate (1-5 years’ experience)

About this course

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by Unsupervised Learning with Python expert instructor, participants will learn about and explore:
  • Unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more
  • Building your own neural network models using modern Python libraries
  • Practical examples show you how to implement different machine learning and deep learning techniques
  • Using cluster algorithms to identify and optimize natural groups of data
  • Exploring advanced non-linear and hierarchical clustering in action
  • Soft label assignments for fuzzy c-means and Gaussian mixture models
  • Detecting anomalies through density estimation
  • Performing principal component analysis using neural network models
  • Creating unsupervised models using GANs

Course breakdown / modules

  • Technical requirements
  • Why do we need machine learning?
  • Types of machine learning algorithm
  • Why Python for data science and machine learning?

  • Technical requirements
  • Introduction to clustering
  • K-means
  • Analysis of the Breast Cancer Wisconsin dataset
  • Evaluation metrics
  • K-Nearest Neighbors
  • Vector Quantization

  • Technical requirements
  • Spectral clustering
  • Mean shift
  • K-medoids
  • 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

  • Technical requirements
  • Soft clustering
  • Fuzzy c-means
  • Gaussian mixture 

  • Technical requirements
  • Probability density functions
  • Histograms
  • Kernel density estimation (KDE)
  • Anomaly detection
  • One-class support vector machines
  • Anomaly detection with Isolation Forests

  • Technical requirements
  • Principal Component Analysis (PCA)
  • Independent Component Analysis
  • Topic modeling with Latent Dirichlet Allocation

  • Technical requirements
  • Autoencoders
  • Hebbian-based principal component analysis
  • Unsupervised deep belief networks

  • Technical requirements
  • Generative adversarial networks
  • Self-organizing maps