Reducing Complexity in Data

Focused View

Janani Ravi

3:20:12

22 View

01 - Course Overview.mp4

02:16

02 - Module Overview.mp4

01:13

03 - Prerequisites and Course Outline.mp4

06:12

04 - The Curse of Dimensionality.mp4

06:12

05 - Overfitting and the Bias-variance Trade-off.mp4

07:18

06 - Techniques to Reduce Complexity.mp4

03:51

07 - Choosing the Right Technique.mp4

04:41

08 - Drawbacks of Reducing Complexity.mp4

01:35

09 - Demo - The Diabetes Dataset - Exploration.mp4

06:39

10 - Demo - Establishing a Baseline Model.mp4

02:28

11 - Demo - The Boston Housing Prices Dataset - Exploration.mp4

03:31

12 - Demo - Kitchen Sink Regression to Establish a Baseline Model.mp4

05:35

13 - Summary.mp4

01:43

14 - Module Overview.mp4

01:13

15 - Statistical Techniques for Feature Selection.mp4

04:02

16 - Conceptual Overview of Different Feature Selection Techniques.mp4

05:11

17 - Demo - Selecting Features Using a Variance Threshold.mp4

07:27

18 - Demo - Selecting K Best Features Using Chi2 Analysis.mp4

04:24

19 - Demo - Setting up Helper Functions for Feature Selection.mp4

04:39

20 - Demo - Find the Right Value for K Using Chi2 Analysis.mp4

03:10

21 - Demo - Find the Right Value for K Using ANOVA.mp4

03:09

22 - Demo - Select Features Using Percentiles and Mutual Information Analysis.mp4

03:28

23 - Demo - Dictionary Learning on Handwritten Digits.mp4

06:33

24 - Summary.mp4

01:38

25 - Module Overview.mp4

02:13

26 - Understanding Principal Components Analysis.mp4

07:46

27 - Demo - Performing PCA to Reduce Dimensionality.mp4

04:01

28 - Demo - Building Linear Models Using Principal Components.mp4

04:12

29 - Understanding Factor Analysis.mp4

02:32

30 - Demo - Applying Factor Analysis to Reduce Dimensionality.mp4

05:45

31 - Understanding Linear Discriminant Analysis.mp4

02:29

32 - Demo - Performing Linear Discriminant Analysis to Reorient Data.mp4

06:12

33 - Summary.mp4

01:09

34 - Module Overview.mp4

02:47

35 - Understanding Manifold Learning.mp4

05:29

36 - Demo - Generate Manifold and Set up Helper Functions.mp4

05:54

37 - Demo - Manifold Learning Using Multidimensional Scaling and Spectral Embedding.mp4

04:07

38 - Demo - Manifold Learning Using t-SNE and Isomap.mp4

02:59

39 - Demo - Manifold Learning Using Locally Linear Embedding.mp4

02:59

40 - Demo - Performing Kernel PCA to Reduce Complexity in Non Linear Data.mp4

07:11

41 - Summary.mp4

01:14

42 - Module Overview.mp4

01:22

43 - K-means Model Stacking.mp4

02:46

44 - Demo - Classifying Image with Original Features.mp4

04:28

45 - Demo - Transforming Data Using K-means Cluster Centers.mp4

03:44

46 - Autoencoding.mp4

07:34

47 - Demo - Prepare Image Data to Feed an Autoencoder.mp4

05:02

48 - Demo - Using Autoencoders to Learn Efficient Representations of Data.mp4

06:04

49 - Summary and Further Study.mp4

02:05

File.zip

Description

This course covers several techniques used to optimally simplify data used in supervised machine learning applications ranging from relatively simple feature selection techniques to very complex applications of clustering using deep neural networks.

What You'll Learn?

Machine learning techniques have grown significantly more powerful in recent years, but excessive complexity in data is still a major problem. There are several reasons for this - distinguishing signal from noise gets harder with more complex data, and the risks of overfitting go up as well. Finally, as cloud-based machine learning becomes more and more popular, reducing complexity in data is crucial in making training more affordable. Cloud-based ML solutions can be very expensive indeed.

In this course, Reducing Complexity in Data you will learn how to make the data fed into machine learning models more tractable and more manageable, without resorting to any hacks or shortcuts, and without compromising on quality or correctness.

First, you will learn the importance of parsimony in data, and understand the pitfalls of working with data of excessively high-dimensionality, often referred to as the curse of dimensionality.

Next, you will discover how and when to resort to feature selection, employing statistically sound techniques to find a subset of the features input based on their information content and link to the output.

Finally, you will explore how to use two advanced techniques - clustering, and autoencoding. Both of these are applications of unsupervised learning used to simplify data as a precursor to a supervised learning algorithm. Each of them often relies on a sophisticated implementation such as deep learning using neural networks.

When you’re finished with this course, you will have the skills and knowledge of conceptually sound complexity reduction needed to reduce the complexity of data used in supervised machine learning applications.

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Data Science

Janani Ravi

Instructor's Courses

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.