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Hands-on Machine Learning with Scikit-Learn
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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.

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Overview

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 ...Show More

Scikit-Learn is a powerful library that provides a handful of supervised and unsupervised learning algorithms. If you’re serious...Show More

Content

36 Lessons2 Quizzes5 Challenges

Preliminaries

2 Lessons

Get familiar with Scikit-learn, its tools, practical use, and required prerequisites.

Introduction

Prerequisites

Working with Datasets

3 Lessons

Get started with loading datasets, generating synthetic data, and essential data preprocessing techniques.

Load built-in dataset

Generate synthetic dataset

Data Preprocessing

Feature Engineering

5 Lessons

Go hands-on with feature selection, extraction, missing value handling, PCA, and pipelines.

Feature Selection

Feature Extraction

Missing Value

PCA(Principal Component Analysis)

Pipeline

General Concepts

3 Lessons

Break down complex ideas of metrics selection and parameter searching for machine learning.

Metrics

Parameter Searching

Quiz

Linear Regression

3 Lessons

Solve problems in building and evaluating linear regression models using Scikit-Learn.

Linear Regression

Challenge - Auto Insurance Prediction

Challenge Solution Review

Logistic Regression

3 Lessons

Tackle logistic regression, preprocess data, and evaluate binary classification models.

Logistic Regression

Challenge - Classification Prediction With Data Preprocessing

Challenge Solution Review

Support Vector Machine

3 Lessons

Practice using SVMs for classifying both linear and non-linear data effectively.

Support Vector Machine

Challenge - Classification of non-linear data

Challenge Solution Review

Tree Model and Ensemble Method

5 Lessons

Step through decision trees, gradient boosting, parameter tuning, and random forest models using Scikit-Learn.

Decision Tree

Gradient Boosting Tree

Challenge - GBDT Parameter Fine-Tune

Challenge Solution Review

Random Forest

Unsupervised Learning

2 Lessons

Look at key unsupervised learning techniques: K-means clustering and t-SNE for data visualization.

K-means Clustering

t-SNE

10.

Deep Learning

3 Lessons

Go hands-on with building and fine-tuning neural networks using Scikit-Learn's MLPClassifier.

Neural Network

Challenge - Build a Neural Network and fine-tune it

Challenge Solution Review

11.

Others

3 Lessons

Apply your skills to Naive Bayes classifiers and K-Nearest Neighbors for predictive modeling.

Naive Bayes

KNN (K-Nearest Neighbors)

Quiz

12.

What's Next

1 Lessons

Deepen your knowledge of next steps in AI and recommended learning resources.

Where to Next?

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Neko Yan

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