Choosing the Right Machine Learning Algorithm

Choosing the Right Machine Learning Algorithm, available at $44.99, has an average rating of 3.45, with 40 lectures, 6 quizzes, based on 12 reviews, and has 85 subscribers.

You will learn about 1. Overview of different machine learning algorithms 2. Pros and cons of different machine learning algorithms 3. Practical use cases of different machine learning algorithms 4. Figure out which machine learning algorithms works for a particular business problem This course is ideal for individuals who are This course is targeted at people who are in early stages of their data science career and for managers/executives who want to get an overview of different machine learning algorithms It is particularly useful for This course is targeted at people who are in early stages of their data science career and for managers/executives who want to get an overview of different machine learning algorithms.

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Summary

Title: Choosing the Right Machine Learning Algorithm

Price: $44.99

Average Rating: 3.45

Number of Lectures: 40

Number of Quizzes: 6

Number of Published Lectures: 40

Number of Published Quizzes: 6

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

1. Overview of different machine learning algorithms
2. Pros and cons of different machine learning algorithms
3. Practical use cases of different machine learning algorithms
4. Figure out which machine learning algorithms works for a particular business problem

Who Should Attend

This course is targeted at people who are in early stages of their data science career and for managers/executives who want to get an overview of different machine learning algorithms

Target Audiences

This course is targeted at people who are in early stages of their data science career and for managers/executives who want to get an overview of different machine learning algorithms

This course covers the basics of the following algorithms:

Linear Regression
Logistics Regression
Decision Trees
K-Means
PCA
Support Vector Machines
Random Forest
Apriori
Adaptive Boosting
Naïve Bayes
Neural Networks

For each of these, the course dives into the underlying concept, pros & cons, and the different practical business use cases where each of these algorithms work well. For those interested in getting their hands dirty, there are also sample implementations of the algorithms in Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Choosing ML Algorithm

Chapter 2: Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Implementation of Linear Regression

Lecture 3: Pros and Cons of Linear Regression

Lecture 4: Use Cases of Linear Regression

Chapter 3: Logistics Regression

Lecture 1: Introduction to Logistics Regression

Lecture 2: Pros and Cons of Logistics Regression

Lecture 3: Use Cases of Logistics Regression

Chapter 4: Decision Trees

Lecture 1: Introduction to Decision Trees

Lecture 2: Pros and Cons of Decision Trees

Lecture 3: Use Cases of Decision Trees

Chapter 5: K-Means

Lecture 1: Introduction to K-Means

Lecture 2: Pros and Cons of K-Means

Lecture 3: Use Cases of K-Means

Lecture 4: Implementation of K-Means

Chapter 6: Principal Component Analysis

Lecture 1: Introduction to Principal Component Analysis

Lecture 2: Pros and Cons of Principal Component Analysis

Lecture 3: Use Cases of Principal Component Analysis

Lecture 4: Implementing Principal Component Analysis

Lecture 5: PCA to Speed up ML Algorithms

Lecture 6: PCA for Data Visualization

Chapter 7: Support Vector Machines

Lecture 1: Introduction to Support Vector Machines

Lecture 2: Pros and Cons of Support Vector Machines

Lecture 3: Use Cases of Support Vector Machines

Lecture 4: Implementing Support Vector Machines

Chapter 8: Random Forest

Lecture 1: Introduction to Random Forest

Lecture 2: Pros and Cons of Random Forest

Chapter 9: Apriori

Lecture 1: Introduction to Apriori

Lecture 2: Implementing Apriori

Chapter 10: Adaptive Boosting

Lecture 1: Introduction to Adaptive Boosting

Chapter 11: Naïve Bayes

Lecture 1: Introduction to Naïve Bayes

Lecture 2: Pros and Cons of Naïve Bayes

Lecture 3: Use Cases of Naïve Bayes

Lecture 4: Implementing Naïve Bayes

Chapter 12: Neural Networks

Lecture 1: Introduction to Neural Networks

Lecture 2: Top Neural Networks Architectures

Lecture 3: Pros and Cons of Neural Networks

Lecture 4: Use Cases of Neural Networks

Lecture 5: Implementing Neural Networks

Instructors

TechTales Inc.
Educator & Trainer in Digital Tech

Rating Distribution

1 stars: 3 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 1 votes
5 stars: 4 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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