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Hands-on Machine Learning with Scikit-Learn - AI-Powered Course
Intermediate
5h
Hands-on Machine Learning with Scikit-Learn
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
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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.
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
1.
Preliminaries
2 Lessons
Get familiar with Scikit-learn, its tools, practical use, and required prerequisites.
2.
Working with Datasets
3 Lessons
Get started with loading datasets, generating synthetic data, and essential data preprocessing techniques.
3.
Feature Engineering
5 Lessons
Go hands-on with feature selection, extraction, missing value handling, PCA, and pipelines.
4.
General Concepts
3 Lessons
Break down complex ideas of metrics selection and parameter searching for machine learning.
5.
Linear Regression
3 Lessons
Solve problems in building and evaluating linear regression models using Scikit-Learn.
6.
Logistic Regression
3 Lessons
Tackle logistic regression, preprocess data, and evaluate binary classification models.
7.
Support Vector Machine
3 Lessons
Practice using SVMs for classifying both linear and non-linear data effectively.
8.
Tree Model and Ensemble Method
5 Lessons
Step through decision trees, gradient boosting, parameter tuning, and random forest models using Scikit-Learn.
9.
Unsupervised Learning
2 Lessons
Look at key unsupervised learning techniques: K-means clustering and t-SNE for data visualization.
10.
Deep Learning
3 Lessons
Go hands-on with building and fine-tuning neural networks using Scikit-Learn's MLPClassifier.
11.
Others
3 Lessons
Apply your skills to Naive Bayes classifiers and K-Nearest Neighbors for predictive modeling.
12.
What's Next
1 Lessons
Deepen your knowledge of next steps in AI and recommended learning resources.
Certificate of Completion
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Course Author:
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