
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

If you have stumbled upon this course, chances are you’ve heard of Deep Learning, which is all the rage these days. However, Deep Learning is only a subset of the field of Machine Learning. There are many traditional algorithms used in different fields, such as finance, education, medicine, and basic science. Deep Learning is not a silver bullet; many scenarios and tasks still require traditional Machine Learning.

# Why do we still need traditional Machine Learning?

People may wonder why we need traditional Machine Learning when Deep Learning is so good. The simple answer is that Deep Learning has its limits.

- Deep Learning is a `black-box`, which means we don't know why it gives such outputs based on our inputs. Some traditional Machine Learning is interpretable, such as linear regression and decision trees. Interpretability is important in some areas.
- Deep Learning generally requires a lot of data. However, in the real world, the data for some tasks is small. Traditional Machine Learning can easily handle small data.
- Some problems are relatively simple, and the benefits brought by deep learning are not significant. Simple solutions can be just as effective with less input.
- There are some problems that only traditional Machine Learning can deal with.

`Scikit-learn` is just what we need.

Scikit-learn (formerly *scikits.learn* and also known as `sklearn`) is a free, software Machine Learning library for the Python programming language. It features various classification, regression and clustering algorithms including `support vector machines`, `random forests`, `gradient boosting`, `k-means`, and `DBSCAN`. In addition, `sklearn` also provides feature extraction, feature selection, model evaluation, model selection, parameter tuning, dimension reduction, and so on. These are essential for a complete Machine Learning project.

In this course, we will not cover the theory or mathematics behind any of these algorithms. Some background knowledge will be introduced in each lesson, but the main purpose is to help you use this library to deal with the real problems. 

# Why do we choose `sklearn`?

- It's easy to learn and use.
- It covers all aspects of Machine Learning, even Deep Learning.
- It's very versatile and powerful.
- Detailed documentation, open-source, and active community.
- It is the most widely used Machine Learning toolkit.

# What you will learn from this course?

- Some models for supervised learning, such as `Logistic Regression`, `SVM`, and `Naive Bayes`
- Tree-based models such as `decision tree` and `GBDT`
- Clustering methods such as `kmeans`
- Feature engineering such as `feature selection`, `feature extraction`, and `dimension reduction`
- Data preprocessing
- Model evaluation
- Hyperparameter searching
- Simple neural network

Introduction

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

Introduction