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):

Linear Regression
Polynomial Regression
Multiple Linear Regression
Logistic Regression
Support Vector Machines
Decision Trees
Random Forest
Principle Component Analysis
Gradient Boosting
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

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

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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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