Machine Learning in Python Bootcamp

  • 4 Courses | 10h 22m 6s
  • 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 November 2nd at 11 AM ET. In this sessionLucas Kelly discusses the characteristics of supervised and unsupervised machine learning, clustering and its applications, and using k-means for clustering.
2 videos | 2h 7m 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 November 3rd at 11 AM ET. In this session Lucas Kelly 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.
2 videos | 2h 42m 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 November 4th at 11 AM ET. In this session Lucas Kelly discusses applying cross-validation to understand what is the optimal model accuracy, using hyperparameters and GridSearch, logistic regression and its applications.
2 videos | 1h 31m 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 November 5th at 11 AM ET. In this session Lucas Kelly 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.
2 videos | 1h 52m available Badge
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