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Building Machine Learning Solutions with scikit-learn

Janani Ravi

What You'll Learn

Design and implement common machine learning solutions using scikit-learn

Evaluation and validation of scikit-learn machine learning models

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What's inside

Seven courses

Building Your First scikit-learn Solution

(2 hours)

This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.

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Building Classification Models with scikit-learn

(2 hours)

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.

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Building Regression Models with scikit-learn

(2 hours)

This course covers regression techniques, including ordinary least squares, lasso, ridge, and Elastic Net, as well as advanced techniques like Support Vector Regression and Stochastic Gradient Descent Regression.

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Building Clustering Models with scikit-learn

(2 hours)

This course covers several important techniques used to implement clustering in scikit-learn, including the K-means, mean-shift, and DBScan clustering algorithms, as well as the role of hyperparameter tuning and performing clustering on image data.

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Employing Ensemble Methods with scikit-learn

(2 hours)

This course covers ensemble learning solutions in scikit-learn, from random forests to adaptive and gradient boosting, model stacking, and hyperparameter tuning.

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Preparing Data for Modeling with scikit-learn

(3 hours)

This course covers important steps in data pre-processing, including standardization, normalization, novelty and outlier detection, pre-processing image and text data, as well as explicit kernel approximations.

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Scaling scikit-learn Solutions

(2 hours)

This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.

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Time

1128 hours

Via

Pluralsight

Institution

Instructor

Janani Ravi

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