Unsupervised Learning in Machine Learning > Data Preprocessing for Machine Learning

Finding Machine Learning Datasets

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  • Datasets for Machine Learning are widely available from a variety of sources.
  • Datasets from Kaggle.
  • Machine Learning Repository at UC Irvine.
  • Datasets are available through Amazon Web Services (AWS).
  • Dataset Search Engine by Google.
  • Datasets from Microsoft.
  • This is a fantastic collection of public datasets.
  • Datasets for Computer Vision.
  • Dataset created with Scikit-learn.

  • Kaggle. A data science community offering tools and resources, including machine learning
  • datasets of all kinds donated by others.
  • Google Dataset Search.
  • UCI Machine Learning Repository.
  • OpenML.
  • DataHub.
  • Papers with Code.
  • VisualData.
  • Data.gov.

  • Google Dataset Search.
  • Kaggle.
  • Data.Gov.
  • Datahub.io.
  • UCI Machine Learning Repository.
  • Earth Data.
  • CERN Open Data Portal.
  • Global Health Observatory Data Repository.

  • Palmer Penguin Dataset.
  • Bike Sharing Demand Dataset.
  • Wine Classification Dataset.
  • Boston Housing Dataset.
  • Ionosphere Dataset.
  • Fashion MNIST Dataset.
  • Cats vs Dogs Dataset.
  • Breast Cancer Wisconsin (Diagnostic) Dataset.

You can take these methods to download datasets that are utilized in projects:

  • Go to File > Open and select your project.
  • Navigate to the folder containing the datasets.
  • Click Download after selecting the datasets you require.

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Reviews

P
Prabhat Yadav
5

Superb course content and easy to understand.

M
Malay Mehta
5

Good Course

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Course Content

  1. Introduction to Machine Learning
    1m 3s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
    4m 43s
  4. Environment Setup part 3
    2m 33s

  1. Data Wrangling
    12m 26s
  2. Importing Libraries and Dataset
    11m 19s
  3. Handling Missing Data
    11m 51s
  4. Handling Missing Data - Practical
    17m 3s
  5. Encoding Categorical Data
    6m 34s
  6. Encoding Categorical Data - Practical
    8m 3s
  7. Splitting Dataset
    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
    6m 18s
  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis
    9m
  13. Plotting Graphs - Part 1
    9m
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
  2. Sampling Methods
    19m 43s
  3. Underfitting and Overfitting in Models
    6m 7s
  4. Variance and Bias
    9m 49s

  1. Distance Metrics
    6m 2s
  2. K-Means Clustering
    11m 3s
  3. K-Means Clustering - Practical
    9m 49s
  4. Hierarchical Clustering - Agglomerative , Divisive
    4m 38s
  5. Agglomerative Clustering - Practical
    4m 44s
  6. Divisive Clustering - Practical
    14m 42s
  7. DBscan Spatial Clustering
    8m 49s
  8. FP Growth
    7m 43s
  9. Assignment of Unsupervised Learning Algorithms
    2m 41s

  1. Overview of Dimensionality Reduction
    4m 25s
  2. Principal Component Analysis
    33m 44s
  3. Princinpal Component Analysis - Practical
    6m 34s
  4. Linear Discriminant Analysis
    18m 51s
  5. Linear Discriminant Analysis - Practical
    6m 42s
  6. Assignment of Dimensionality Reduction
    55s

  1. Advance Trends in Machine Learning
    10m 13s

  1. Course Summary
    3m 51s

  1. Interview Questions part 1
    19m 22s
  2. Interview Questions part 2
    12m 23s
  3. Interview Questions part 3
    15m 20s

  1. Career Guidelines
    1m 28s
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