Machine Learning Guide- Learn Machine Learning Algorithms

Machine Learning Guide: Learn Machine Learning Algorithms, available at $19.99, has an average rating of 3.05, with 12 lectures, based on 204 reviews, and has 10081 subscribers.

You will learn about Fundamental concepts of AI and applications of machine learning Learn different classification and regression techniques Learn clustering, including k-means and k-nearest Neighbors Learn Decision Trees to decode classification Learn Regression analysis to create trend lines Understand Bias/Variance to improve your machine learning model This course is ideal for individuals who are Developers or Technology consultants or Engineers or Computer scientists or Statisticians It is particularly useful for Developers or Technology consultants or Engineers or Computer scientists or Statisticians.

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Summary

Title: Machine Learning Guide: Learn Machine Learning Algorithms

Price: $19.99

Average Rating: 3.05

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Fundamental concepts of AI and applications of machine learning

Learn different classification and regression techniques

Learn clustering, including k-means and k-nearest Neighbors

Learn Decision Trees to decode classification

Learn Regression analysis to create trend lines

Understand Bias/Variance to improve your machine learning model

Who Should Attend

Developers

Technology consultants

Engineers

Computer scientists

Statisticians

Target Audiences

Developers

Technology consultants

Engineers

Computer scientists

Statisticians

Artificial Intelligence is becoming progressively more relevant in today’s world. The rise of AI has the potential to transform our future more than any other technology.By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots.

Machine learning is one of the most important areas of Artificial Intelligence. Machine learning provides developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. It can be applied across many industries to increase profits, reduce costs, and improve customer experiences.

In this course I’m going to provide you with a comprehensive introduction to the field of machine learning. You will learn how to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. Also i’m going to offer you a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics. You’ll discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. In addition you’ll learn how to drive innovation by combining data, technology and design to solve real problems at an enterprise scale.

This course is focused on helping you drive concrete business decisions through applications of artificial intelligence and machine learning. It makes the fundamentals and algorithms of machine learning accessible to students in statistics, computer science, mathematics, and engineering. This means plain-English explanations and no coding experience required. This is the best practical guide for business leaders looking to get true value from the adoption of machine learning technology.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Defining Machine Learning

Lecture 1: Defining Machine Learning (Part 1)

Lecture 2: Defining Machine Learning (Part 2)

Chapter 3: Core Concepts

Lecture 1: Core Concepts (Part 1)

Lecture 2: Core Concepts (Part 2)

Chapter 4: Algorithms

Lecture 1: Decision Trees

Lecture 2: K-Means Clustering

Lecture 3: K-Nearest Neighbor

Lecture 4: Naive Bayes

Lecture 5: Regression

Lecture 6: Best Practices and Applications

Chapter 5: Conclusion

Lecture 1: Conclusion

Instructors

Grid Wire
Freelancer

Rating Distribution

1 stars: 24 votes

2 stars: 21 votes

3 stars: 52 votes

4 stars: 30 votes

5 stars: 77 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!