## The Learning Lobby

# Best Machine Learning Online Courses to Take in 2023

- November 30, 2022
- Posted by: elektev
- Category: Recommended Courses

Machine learning has gotten very popular in recent times. As a prominent subfield of AI, more industries are finding new and interesting applications for these algorithms daily. For those looking to add to their repertoire of skills and future-proof themselves, the benefits of picking up machine learning now are massive.

Thankfully, you don’t have to attend a university or spend tons of money to get a degree in this field if you don’t want to. By finding the best machine learning online courses to take in 2023, it’ll be easy to acquire this essential skill. You can then implement the concepts and principles gained into projects.

Here are the top machine learning courses to check out right now.

**Best Machine Learning Online Courses to Take in 2023**

- Learning From Data (Introductory Machine Learning) – edX
- Machine Learning 2020: Complete Maths for Machine Learning – Udemy
- Learning Path: Statistics for Machine Learning – Udemy
- Machine Learning: Unsupervised Learning – Udacity
- Machine Learning with AWS AI and IBM Watson – Udemy

**Learning From Data (Introductory Machine Learning)**** (edX)**

This is an introductory course in machine learning that’ll help auditors cover the fundamental aspects of the theories, algorithms, and applications in the field. The knowledge gained here can be put to medical, scientific, commercial, and financial applications.

There are 18 lectures in this course and it’ll run for 10 weeks.

By the end of this program, you’ll be able to:

- Identify the basic theoretical principles and algorithms in machine learning;
- Understand the basic applications of machine learning;
- Establish a solid connection between theory and practice in the field;
- Understand the heuristic and mathematical features of machine learning and identify potential real-world application situations.

**Course Prerequisite(s)**

- Basic programming knowledge;
- Basic Calculus;
- Basic Matrices;
- Basic Probability.

**Machine Learning 2020: Complete Maths for Machine Learning**** (Udemy)**

This is another great beginner course that’ll help auditors explore the relevance of math in machine learning. A solid understanding of mathematics is essential in this area because it’s the foundation of data science.

The 2020 Complete Maths for Machine Learning course will cover Calculus, Linear Algebra, Algebra Foundations, and Probability from scratch.

By the end of this program, you’ll be able to:

- Intuitively master every mathematical concept relevant to the basics of machine learning;
- Understand the role of the various concepts of Calculus in enhancing machine learning processes;
- Master Linear Algebra in machine learning;
- Recognize the impact of matrix transformation in data optimization;
- Understand the relevance of Algebraic Equations in machine learning.

**Course Prerequisite(s)**

- There are none.

**Learning Path: Statistics for Machine Learning**** (Udemy)**

This course looks at the most innovative ways that machine learning enthusiasts can tackle the more complex statistical problems in the field. To build auditors up to this level, the course will first extensively go over the relevant statistical terminology in machine learning.

Once all bases have been covered, auditors are then slowly introduced to more complex statistical computations, algorithms, and real-world situations where they prove useful.

The course is broadly divided into two segments; Fundamentals of Statistical Modeling and Machine Learning Techniques and Advanced Statistics for Machine Learning.

By the end of this program, you’ll be able to:

- Master statistical terminology in machine learning;
- Use credit data to execute Logistic Regression;
- Device practical solutions for both simple linear and multilinear regressions;
- Analytically evaluate logistic regression and random forest;
- Know the various types of Unsupervised Learning;
- Master the concepts of artificial neural networks.

**Course Prerequisite(s)**

- Knowledge of R programming;
- Knowledge of Python.

**Machine Learning: Unsupervised Learning**** (Udacity)**

Unsupervised Learning is an aspect of machine learning that most people are introduced to daily. It’s what’s responsible for the pin-point recommendations you see on mega-platforms like Amazon, Netflix, and, more recently, even Facebook.

Machine Learning: Unsupervised Learning is an introductory course that lets auditors see how algorithms analyze data, search for patterns and use that information to make increasingly accurate predictions.

This course delves into several basic concepts of Unsupervised Learning such as feature selection and transformation, randomized optimization, and clustering in data processing.

It contains six lessons which can be covered within 30 days.

By the end of this course, auditors will be able to:

- Understand the various concepts of clustering;
- Implement randomized optimization in machine learning;
- Perform feature transformation tasks;
- Understand information theory and its real-world applications.

**Course Prerequisite(s)**

- Knowledge of probability;
- Knowledge of statistics;
- Knowledge of Python programming.

**Machine Learning with AWS AI and IBM Watson**** (Udemy)**

For those interested in delving into the world of AI directly, this introductory machine learning course offers an excellent segue into the space. This course provides a peek behind the curtain into some of the most effective machine learning projects in operation currently.

The hands-on program will teach you how to optimize and deploy various machine services features like Amazon AWS and IBM Bluemix. It also identifies the most efficient ways to incorporate cloud-based services into Internet of Things, Web, Desktop, and Android applications.

By the end of this program, auditors will be able to:

- Build ChatBots with Watson Assistant;
- Understand the capabilities of Watson API;
- Master cognitive computing;
- Master visual content classification with Watson Visual Recognition;
- Know how to use Amazon Comprehend and Watson Natural Language to perform advanced text analysis;
- Understand how to use Amazon Sagemaker to build, train, and deploy scalable machine learning models.

**Course Prerequisites:**

- A valid AWS and IBM Bluemix account.
- A background in computer science is helpful but optional.

**Bottom Line**

The world of machine learning is a fun and exciting space that has limitless potential. From enhancing efficiency and productivity to improving decision-making and data entry, this field is poised to change the world as we know it.

Listed above are the best machine learning online courses to take in 2023 as they help you see machine learning through unique lenses. With this knowledge, you can broaden your competencies and explore possible applications of the field in various industries intensively.

If you’d like to brush up on your programming skills before embarking on this amazing journey, here are some great Python courses found on elektev.com you can take.