Practical Machine Learning with Scikit-Learn
Practical Machine Learning with Scikit-Learn, available at Free, has an average rating of 4.35, with 5 lectures, based on 325 reviews, and has 15452 subscribers.
You will learn about How to implement regression, classification and boosting algorithms Which algorithms work best for a given dataset Data preprocessing This course is ideal for individuals who are People looking to get into AI but don't know where to start or People who want to build accurate models as quickly as possible It is particularly useful for People looking to get into AI but don't know where to start or People who want to build accurate models as quickly as possible.
Enroll now: Practical Machine Learning with Scikit-Learn
Summary
Title: Practical Machine Learning with Scikit-Learn
Price: Free
Average Rating: 4.35
Number of Lectures: 5
Number of Published Lectures: 5
Number of Curriculum Items: 5
Number of Published Curriculum Objects: 5
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- How to implement regression, classification and boosting algorithms
- Which algorithms work best for a given dataset
- Data preprocessing
Who Should Attend
- People looking to get into AI but don't know where to start
- People who want to build accurate models as quickly as possible
Target Audiences
- People looking to get into AI but don't know where to start
- People who want to build accurate models as quickly as possible
Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it’s most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.
Algorithms we’ll go over (in order):
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Linear Regression
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Polynomial Regression
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Multiple Linear Regression
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Logistic Regression
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Support Vector Machines
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Decision Trees
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Random Forest
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Principle Component Analysis
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Gradient Boosting
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XGBoost
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Data Preprocessing
Chapter 2: Regression
Lecture 1: Regression
Chapter 3: Classification
Lecture 1: Classification
Chapter 4: Boosting and Optimization
Lecture 1: Boosting and Optimization
Instructors
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Adam Eubanks
Self Taught Programmer And Learning Enthusiast
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 8 votes
- 3 stars: 46 votes
- 4 stars: 118 votes
- 5 stars: 152 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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