Introduction to ML Classification Models using scikit-learn

Introduction to ML Classification Models using scikit-learn, available at $44.99, has an average rating of 3.95, with 18 lectures, based on 35 reviews, and has 745 subscribers.

You will learn about Have a broad understanding of ML and hands on experience with building classification models using Support Vector Machines, Decision Trees and Random Forests in Pythons scikit-learn This course is ideal for individuals who are Developers and data scientists who wish to learn how to build classification models in ML It is particularly useful for Developers and data scientists who wish to learn how to build classification models in ML.

Enroll now: Introduction to ML Classification Models using scikit-learn

Summary

Title: Introduction to ML Classification Models using scikit-learn

Price: $44.99

Average Rating: 3.95

Number of Lectures: 18

Number of Published Lectures: 18

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Have a broad understanding of ML and hands on experience with building classification models using Support Vector Machines, Decision Trees and Random Forests in Pythons scikit-learn

Who Should Attend

Developers and data scientists who wish to learn how to build classification models in ML

Target Audiences

Developers and data scientists who wish to learn how to build classification models in ML

This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models.?Basic ML concepts of ML are explained,?including Supervised and Unsupervised Learning;?Regression and Classification;?and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python.

The Intro to ML?Classification Models course is meant for developers or data scientists (or anybody else)?who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification.?

Course Curriculum

Chapter 1: Introduction

Lecture 1: You, This Course and Us

Lecture 2: Source Code and PDFs

Lecture 3: Install Anaconda

Chapter 2: What is ML?

Lecture 1: What is Machine Learning?

Lecture 2: Types of Machine Learning – Supervised Learning and Linear Regression

Lecture 3: Types of Machine Learning – Logistic Regression and Unsupervised Learning

Chapter 3: Support Vector Machines (SVMs)

Lecture 1: What is an SVM? How do they work?

Lecture 2: SVM Lab (1): Loading and examining our data set

Lecture 3: SVM Lab (2): Building and tweaking our SVM classification model

Chapter 4: Decision Trees

Lecture 1: What is a Decision Tree?

Lecture 2: Building a Decision Tree – Decision Tree Learning

Lecture 3: Building a Decision Tree – Information Gain and Gini Impurity

Lecture 4: Decision Trees Lab (1): Building our first Decision Tree

Lecture 5: Decision Trees Lab (2): Viewing and tweaking our Decision Tree

Chapter 5: Overfitting – the Bane of Machine Learning

Lecture 1: What is Overfitting? And Why is it a Problem?

Lecture 2: Avoiding Overfitted Models – Cross Validation and Regularization

Chapter 6: Ensemble Learning and Random Forests

Lecture 1: Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting

Lecture 2: Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results

Instructors

Loony Corn
An ex-Google, Stanford and Flipkart team

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 7 votes

4 stars: 19 votes

5 stars: 8 votes

Frequently Asked Questions

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