
Gain insights into Scikit-Learn's datasets, feature engineering, linear/logistic regression, and unsupervised learning. Delve into k-means clustering and neural networks to enhance your machine learning expertise.

mk1.tar.gz

data_preprocessing

dataset

decision_tree_classification

feature_extraction

feature_selection

gradient_boost

kmeans

knn_classification

sk_lr_classification

sk_lr_regression

sk_metrics

sk_missing_value

sk_naive_bayes_classification

sk_nn

sk_parameter_search

sk_pca

sk_pipeline

sk_rf

sk_tsne

sk_svm_classification

jupyter_job

python_updated

sk_naive_bayes_classification-copy

Scikit-Learn is a powerful library that provides a handful of supervised and unsupervised learning algorithms. If you’re serious about having a career in machine learning, then scikit-learn is a must know.

In this course, you will start by learning the various built-in datasets that scikit-learn offers, such as iris and mnist. You will then learn about feature engineering and more specifically, feature selection, feature extraction, and dimension reduction.

In the latter half of the course, you will dive into linear and logistic regression where you’ll work through a few challenges to test your understanding. Lastly, you will focus on unsupervised learning and deep learning where you’ll get into k-means clustering and neural networks.

By the end of this course, you will have a great new skill to add to your resume, and you’ll be ready to start working on your own projects that will utilize scikit-learn.

Hands-on Machine Learning with Scikit-Learn

`KNN` stands for the k-nearest neighbor algorithm which is a non-parametric method used for classification and regression. In both cases, the input consists of the `k` closest training examples in the feature space. `KNN` is a type of instance-based learning or lazy learning. Lazy learning means this model doesn't need any training process. Non-parametric means there is no assumption for the distribution of data. Instance-based means you need to store all data points.

# How does the KNN work?

Let's follow the steps below:

- Define `K`. `K` is a hyperparameter, which defines the number of neighbors we used to do the prediction work.
- After defining the `K`, the model is already generated.
- Input a new sample, calculate the distance between the new sample and the whole data set. Some common definitions of distance are the Euclidean distance, Hamming distance, Manhattan distance, and Minkowski distance.
- Pick up the `K` data points that are closest to the new sample.
- If this is a classification task, select the most frequently occurring category from these `K` data points.
- Then, the most frequent category is prediction.

In other words:

- Calculate distance.
- Pick up `K` closest points.
- Vote for class.

In this lesson, we introduce KNN, which is a very simple and intuitive model. 

KNN (K-Nearest Neighbors)

Preliminaries

Working with Datasets

Feature Engineering

General Concepts

Linear Regression

Logistic Regression

Support Vector Machine

Tree Model and Ensemble Method

Unsupervised Learning

Deep Learning

Others

What's Next

KNN (K-Nearest Neighbors)

How does the KNN work?