Hands-on Scikit-learn for Machine Learning [Video]
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Free ChapterGetting Started with a Simple ML Model in Scikit-learn
-
Classification Models
- Linear and Logistic Regression
- Evaluating Classification Models
- Logistic Regression and Evaluation with Scikit-learn
- Decision Trees
- Bagging, Boosting, and Random Forests
- Applying Ensemble Methods with Scikit-learn
- Support Vector Machines
- Applying Support Vector Machines Classifier with Scikit-learn
- Multi-class Classification Example with Scikit-learn
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Supervised Machine Learning – Regression
- Downloading and Inspecting the Dataset
- Handling Categorical Features and Missing Values
- Creating Train and Test Sets and Finding Correlation
- Feature Scaling, Evaluating Regression Models, and Applying Linear Regression
- Regularization Techniques for Regression Analysis
- Applying Random Forest for Regression Analysis
- Multi-Layer Perceptron, Neural Networks, and Applying MLP with Scikit-learn
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Unsupervised Learning —Dimensionality Reduction
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Unsupervised Learning – Clustering
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Improving ML Model Performance
- Handling Missing Values and Data Cleaning
- Handling Missing Values and Scaling Numerical Features
- Handling Outliers and Removing Distribution Skew
- Handling Outliers and Removing Distribution Skew (Continued)
- Deriving Additional Features
- Evaluating Different Models and Cross- Validation
- Model Selection Strategies
- Feature Engineering for Classification
- Model Selection Strategies for Credit Risk Assessment
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Creating Pipelines and Advanced Model Selection
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Handling Text Data with Scikit-learn
About this
video
Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia.
If you’re an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn.
By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.
All the code and supporting files for this course are available on Github at:
https://github.com/PacktPublishing/Hands-on-Scikit-learn-for-Machine-Learning-V-
- Publication date:
- August 2018
- Publisher
- Packt
- Duration
- 9 hours 3 minutes
- ISBN
- 9781789137132