Hands-on Machine Learning with Scikit-Learn

Description

In this course, you will learn :

• About the various built-in datasets provided by scikit-learn, such as iris and mnist.
• About feature engineering, specifically feature selection, feature extraction, and dimension reduction.
• You will dive into linear and logistic regression where you will work through a few challenges to test your understanding.
• You will concentrate on unsupervised learning and deep learning, where you will gain knowledge.

Syllabus :

1. Working with Datasets

• Load built-in dataset
• Generate synthetic dataset
• Data Preprocessing

 

2. Feature Engineering

• Feature Selection
• Feature Extraction
• Missing Value
• PCA(Principal Component Analysis)
• Pipeline

 

3. General Concepts

• Metrics
• Parameter Searching

 

4. Linear Regression

 

5. Logistic Regression

 

6. Support Vector Machine

 

7. Tree Model and Ensemble Method

• Decision Tree
• Gradient Boosting Tree
• Challenge - GBDT Parameter Fine-Tune
• Challenge Solution Review
• Random Forest

 

8. Unsupervised Learning

• K-means Clustering
• t-SNE

 

9. Deep Learning

• Neural Network

 

10. Others

• Naive Bayes
• KNN (K-Nearest Neighbors)

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