Janani Ravi, Chetan Prabhu | Duration: 24h 00m | Video: H264 1280x720 | Audio: AAC 48 kHz 2ch | 2,87 GB | Language: English
This skill teaches learners how to build machine learning solutions using Python and the pervasive scikit-learn package, including application of classifcation, regression, and clustering.
* Experience the machine learning workflow as implemented in scikit-learn, and use that workflow to build simple classification, regression, and clustering models.
* Build sophisticated neural network models, apply dimension reduction techniques, and combine model approaches.
* Select the appropriate model for your business problem and data, and evaluate the effectiveness of that model.
Courses in this path
A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)
A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)
A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)
A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)
B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)
B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)
B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)
C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)
C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)
C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)
Homepage
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