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 dont 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 dont 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 dont know where to start

People who want to build accurate models as quickly as possible

Target Audiences

People looking to get into AI but dont 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.

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