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## Video Description

A one-stop solution to test model accuracy with cross-validation

• Optimizing the ridge regression parameter
• Analyze and plot an ROC curve without context
• Dummy Estimators and Persisting models with joblib
• Using k-means for outlier detection

In Detail

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.

This course begins by taking you through videos on linear models; with scikit-learn, you will take a machine learning approach to linear regression. As you progress, you will explore logistic regression. Then you will build models with distance metrics, including clustering. You will also look at cross-validation and post-model workflows, where you will see how to select a model that predicts well. Finally, you'll work with Support Vector Machines to get a rough idea of how SVMs work, and also learn about the radial basis function (RBF) kernel.

1. Chapter 1 : Linear Models with scikit-learn
1. The Course Overview 00:03:43
2. Fitting a Line Through Data 00:05:23
3. Evaluating and Overcoming Shortfalls of the Linear Regression Model 00:08:02
4. Optimizing the Ridge Regression Parameter 00:04:02
5. Using Sparsity to Regularize Models 00:03:24
6. Fundamental Approach to Regularization with LARS 00:03:26
2. Chapter 2 : Linear Models – Logistic Regression
1. Exploring Various Repositories and Datasets 00:05:50
2. Logistic Regression and Confusion Matrix 00:04:25
3. Varying the Classification Threshold in Logistic Regression 00:05:51
4. Analysis and Plotting an ROC Curve Without Context 00:06:36
5. UCI Breast Cancer Dataset 00:03:22
3. Chapter 3 : Building Models with Distance Metrics
1. In a dataset, we observe sets of points gathered together. With k-means, we will categorize all the points into groups, or clusters. 00:08:56
2. Handling Data and Quantizing an Image 00:07:09
3. Finding the Closest Object in the Feature Space 00:03:34
4. Probabilistic Clustering with Gaussian Mixture Models 00:04:07
5. Using k-means for Outlier Detection 00:03:13
6. Using KNN for Regression 00:04:16
4. Chapter 4 : Cross-Validation and Post-Model Workflow
1. Cross-Validation 00:08:23
2. Search with scikit-learn 00:04:03
3. Metrics 00:07:39
4. Dummy Estimators and Persisting Models with joblib 00:03:41
5. Feature Selection 00:06:32
5. Chapter 5 : Support Vector Machines
1. Classifying Data with a Linear SVM 00:05:02
2. Optimizing an SVM 00:05:30
3. Multiclass Classification with SVM 00:03:44
4. Support Vector Regression 00:02:44