Machine Learning in Python Bootcamp

  • 4 Courses | 11h 7m 5s
  • Includes Lab
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Welcome to the Machine Learning in Python Bootcamp channel!   Learn how to mine data and uncover patterns within it during this course. Clustering is a foundational unsupervised machine learning technique that is key to discovering latent patterns and trends. By the end of this course, attendees will learn to identify use cases where clustering is relevant, use Python to perform clustering on real-world and evaluate the results.   Attendees must be comfortable using Python to manipulate data and must know how to create basic visualizations and import data. Prior to attending the live sessions, please install Anaconda.

COURSES INCLUDED

Machine Learning in Python Bootcamp: Session 1 Replay
This is a recorded Replay of the Machine Learning in Python Live session that ran on June 8th at 11 AM ET. In this session Martin Skarzynski discusses the characteristics of supervised and unsupervised machine learning, clustering and its applications, and using k-means for clustering.
1 video | 2h 37m available Badge
Machine Learning in Python Bootcamp: Session 2 Replay
This is a recorded Replay of the Machine Learning in Python Live session that ran on June 9th at 11 AM ET. In this session Martin Skarzynski discusses classification and its use cases, summary and applications of knn algorithm, implementation of the knn algorithm on training data, cross-validation and its use cases.
1 video | 2h 57m available Badge
Machine Learning in Python Bootcamp: Session 3 Replay
This is a recorded Replay of the Machine Learning in Python Live session that ran on June 10th at 11 AM ET. In this session Martin Skarzynski discusses applying cross-validation to understand what is the optimal model accuracy, using hyperparameters and GridSearch, logistic regression and its applications.
1 video | 2h 38m available Badge
Machine Learning in Python Bootcamp: Session 4 Replay
This is a recorded Replay of the Machine Learning in Python Live session that ran on June 11th at 11 AM ET. In this session Martin Skarzynski discusses logistic regression on a training dataset and predict on test, classification performance metrics, transformation of categorical variables for implementation of logistic regression, and implementation of logistic regression on the data.
1 video | 2h 53m available Badge
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