The top free machine learning courses for beginners in 2023 is planned to offer a wide range of topics

According to recent research, the demand for machine learning skills is growing at an exponential rate. This is due to the growing importance of machine learning in a variety of industries, including healthcare, finance, and manufacturing. Upskilling by taking free machine learning courses will keep up with current industry demands.

Machine learning is quickly becoming one of the fast-paced computer science fields. It has the potential to improve the efficiency and intelligence of an infinite number of industries and applications. The application of ML in a job requires skilled and knowledgeable professionals. Chatbots, ad serving, spam filtering, search engines, and fraud detection are just a few examples of how machine learning models are used in everyday life. Machine learning courses, as opposed to data science courses, focus solely on teaching ML algorithms, how they work mathematically, and how to use them in a programming language. Here, I will highlight the top 10 free Machine Learning courses for beginners in 2023:

1. Machine Learning by Andrew Ng (Coursera)

Andrew Ng's course is a popular choice for beginners. It covers the fundamentals of machine learning, including linear regression, logistic regression, neural networks, and more.

The course is offered on platforms like Coursera. With a combination of theory and practical assignments, this course provides learners with a solid understanding of machine learning algorithms and their applications in various domains.

2. Machine Learning Crash Course -Google

Machine Learning Crash Course" by Google is a course that provides a comprehensive introduction to machine learning concepts and techniques. It covers fundamental topics such as linear regression, logistic regression, and neural networks. With interactive exercises and real-world examples, this course allows learners to gain a solid foundation in machine learning and understand its practical applications in various fields.

3. Practical Deep Learning for Coders –  fast.ai

Designed for coders with little to no prior background in machine learning. It emphasizes practical applications of deep learning, covering topics such as image classification and natural language processing. With a hands-on approach and practical projects, this course enables learners to gain valuable experience in implementing deep learning models for real-world tasks.

4. Applied Data Science with Python –  University of Michigan

Applied Data Science with Python" by the University of Michigan is a course available on platforms like Coursera. It focuses on the practical application of data science using Python. Learners will gain hands-on experience in data manipulation, visualization, and machine learning. This specialization covers the entire data science workflow, including data manipulation, visualization, and machine learning.

5. Intro to Machine Learning with PyTorch – Udacity

This course offers an introduction to machine learning using PyTorch, a popular deep-learning framework. It covers topics like linear regression, neural networks, and convolutional networks.

6. Introduction to Artificial Intelligence by Sebastian Thrun and Peter Norvig 

Introduction to Artificial Intelligence" by Sebastian Thrun and Peter Norvig on Udacity is a course that offers a comprehensive introduction to AI. It covers topics like search algorithms, probabilistic inference, and machine learning. With interactive quizzes and hands-on projects, this course provides learners with a strong foundation in AI concepts and prepares them to tackle real-world AI problems.

7. Data Science and Machine Learning Bootcamp with R – Udemy

A comprehensive course that covers data science and machine learning using the R programming language. It provides learners with a wide range of topics, including data manipulation, visualization, and predictive modeling. With hands-on exercises and practical examples, this course equips participants with the skills and knowledge necessary to apply data science and machine learning techniques using R.

8. Machine Learning for All – University of London

Machine Learning for All offered by the University of London on Coursera is a beginner-friendly course that covers the fundamentals of machine learning. It provides a comprehensive introduction to various machine learning concepts, including decision trees, clustering, and evaluation methods. The course is designed to make machine learning accessible to all learners, regardless of their background. With practical examples and hands-on exercises, it enables participants to gain a solid understanding of machine learning principles and develop the skills to apply them in real-world scenarios

9. Mathematics for Machine Learning -Coursera

This course focuses on the mathematical foundations of machine learning algorithms. Covering topics such as linear algebra, calculus, and probability theory, it provides learners with a strong mathematical understanding necessary for machine learning. This course is highly recommended for individuals seeking to deepen their mathematical knowledge and apply it to real-world machine-learning problems.

10. Deep Learning Specialization – deeplearning.ai

This specialization consists of a series of courses covering deep learning and neural networks. It covers topics such as deep neural networks, convolutional networks, and recurrent networks. With five courses in total, it provides a comprehensive understanding of neural networks, hyperparameter tuning, regularization, optimization, and structuring machine learning projects.