Data Distillation: Mastering Dimensionality Reduction Techniques Training Course

Introduction

In today's data-driven world, datasets are often plagued by the "curse of dimensionality," where an overwhelming number of features can lead to complex models, increased training time, and overfitting. Dimensionality reduction is the essential process of transforming data into a lower-dimensional space while preserving its most critical information. This technique is a cornerstone of effective data preprocessing, enabling more efficient and robust machine learning pipelines. This course will provide a comprehensive and practical guide to mastering the most important dimensionality reduction methods.

This five-day training will take you through the theory and practical application of linear and non-linear techniques, from classic methods like Principal Component Analysis (PCA) to cutting-edge manifold learning algorithms. You will learn not only how to apply these techniques but also how to choose the right one for your specific problem. By the end, you will be able to distill complex datasets, improve model performance, and create insightful visualizations, giving you a competitive edge in any data science role.

Duration 5 days

Target Audience This course is designed for data scientists, machine learning engineers, and analysts who work with high-dimensional data and want to improve model efficiency, enhance data visualization, and combat the curse of dimensionality.

Objectives

  • To understand the challenges of high-dimensional data and the concept of the "curse of dimensionality."
  • To differentiate between feature selection and feature extraction methods.
  • To master linear dimensionality reduction using Principal Component Analysis (PCA).
  • To implement and interpret t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualization.
  • To gain expertise in non-linear dimensionality reduction methods like Isomap and Locally Linear Embedding (LLE).
  • To learn how to use dimensionality reduction as a preprocessing step for machine learning models.
  • To evaluate the effectiveness of different dimensionality reduction techniques.
  • To apply dimensionality reduction to a variety of real-world datasets, including images and text.
  • To understand the computational trade-offs and best practices for implementation.
  • To work on a capstone project that applies multiple dimensionality reduction techniques.

Course Modules

Module 1: The Curse of Dimensionality

  • What is dimensionality and why does it matter?
  • The problems with high-dimensional data: increased complexity and computational cost.
  • The phenomenon of sparse data and its impact on models.
  • The distinction between intrinsic and extrinsic dimensions.
  • An overview of the two main approaches: feature selection vs. feature extraction.

Module 2: Linear Dimensionality Reduction: PCA

  • A deep dive into Principal Component Analysis (PCA).
  • The intuition behind finding principal components.
  • The mathematics of PCA: eigenvectors and eigenvalues.
  • Step-by-step implementation of PCA from scratch.
  • A practical guide to using PCA with scikit-learn.

Module 3: Non-Linear Dimensionality Reduction

  • Why linear methods are not always enough.
  • An introduction to manifold learning.
  • A conceptual overview of Isomap.
  • An explanation of Locally Linear Embedding (LLE).
  • A brief discussion on other methods like Multi-dimensional Scaling (MDS).

Module 4: Visualization with t-SNE and UMAP

  • The primary use case for dimensionality reduction: data visualization.
  • A conceptual understanding of t-SNE.
  • How to use t-SNE to create beautiful and insightful scatter plots.
  • An introduction to Uniform Manifold Approximation and Projection (UMAP).
  • A comparison of t-SNE and UMAP for visualization.

Module 5: Practical Applications in Machine Learning

  • The role of dimensionality reduction in a typical ML pipeline.
  • How to use dimensionality reduction to combat overfitting.
  • A practical demonstration of using PCA before a classification model.
  • The impact of dimensionality reduction on training time and memory.
  • Strategies for choosing the optimal number of dimensions.

Module 6: Feature Selection Methods

  • The difference between feature extraction and feature selection.
  • An overview of filter methods.
  • A deep dive into wrapper methods.
  • The concept of embedded methods.
  • A hands-on guide to using feature selection with scikit-learn.

Module 7: Dimensionality Reduction for Images

  • The challenge of high-dimensional image data.
  • Using PCA for facial recognition.
  • A practical example of dimensionality reduction for image compression.
  • Applying autoencoders for dimensionality reduction in images.
  • A discussion of other methods for image data.

Module 8: Dimensionality Reduction for Text Data

  • The high dimensionality of text features.
  • A brief review of text vectorization methods.
  • Applying PCA to a text dataset.
  • Using t-SNE and UMAP to visualize text data.
  • A discussion of Latent Semantic Analysis (LSA).

Module 9: Case Studies

  • A case study on gene expression data.
  • A case study on text data for document classification.
  • A case study on anomaly detection with dimensionality reduction.
  • A case study on social network analysis.
  • A case study on financial data.

Module 10: Advanced Topics

  • The concept of kernel PCA.
  • Probabilistic PCA.
  • Sparse PCA.
  • A brief introduction to non-negative matrix factorization (NMF).
  • A discussion on the latest research.

Module 11: Implementation and Best Practices

  • A guide to the most useful libraries (scikit-learn, TensorFlow).
  • Handling missing values before dimensionality reduction.
  • Scaling data as a crucial preprocessing step.
  • Evaluating the loss of information.
  • A checklist for applying dimensionality reduction techniques.

Module 13: Career Paths and Outlook

  • How dimensionality reduction is used in various industries.
  • The role of dimensionality reduction in data compression.
  • New tools and frameworks for large-scale data.
  • Future trends and research in the field.
  • Final Q&A and course wrap-up.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Data Distillation: Mastering Dimensionality Reduction Techniques Training Course in Namibia
Dates Fees Location Action