Top 5 Machine Learning Courses Free to Improve Your Skills Enroll Now in 2024

Machine learning is revolutionizing the way we interact with technology, making it a crucial skill for anyone looking to advance in the tech industry.In this post, we will explore the Top 5 Machine Learning Courses Free options for 2024 that you can enroll in right now. As the demand for machine learning professionals continues to grow, many individuals are seeking out educational resources to enhance their skills.

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About the Machine Learning Courses Free

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. When applied to business problems, it is known under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field’s methods.

The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.

From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning.

Eligibility Criteria

Top 5 Machine Learning Courses Free For Everyone.

See Also: Datacamp Free Access Week

Why Choose Free Machine Learning Courses?

Accessibility: Free courses offer a low-barrier entry for individuals interested in machine learning.

Flexibility: You can learn at your own pace and schedule.

Quality: Many reputable institutions and platforms offer high-quality free courses.

Foundation Building: These courses provide a solid foundation for further learning and specialization.

Here Are the Top 5 Machine Learning Courses Free

1. Generative AI for Everyone

Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you’ll learn to use generative AI to help in day-to-day work and receive tips on effective prompt engineering, as well as learning how to go beyond prompting for more advanced uses of AI.

You’ll delve into real-world applications and learn common use cases, and get hands-on time with generative AI tools to put your knowledge into action, and gain insight into AI’s impact on both business and society.

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2.CS229 – Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.

The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

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3. Machine Learning with Python

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

Representation, over-fitting, regularization, generalization, VC dimension;
Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

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4. Mathematics for Machine Learning

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

5.Practical Deep Learning

This free course is designed for people (and bunnies!) with some coding experience who want to learn how to apply deep learning and machine learning to practical problems.

Deep learning can do all kinds of amazing things. For instance, all illustrations throughout this website are made with deep learning, using DALL-E 2.

Practical Deep Learning for Coders 2022 part 1, recorded at the University of Queensland, covers topics such as how to:

Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems

Create random forests and regression models

Deploy models

Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face

There are 9 lessons, and each lesson is around 90 minutes long. The course is based on our 5-star rated book, which is freely available online.

You don’t need any special hardware or software — we’ll show you how to use free resources for both building and deploying models. You don’t need any university math either — we’ll teach you the calculus and linear algebra you need during the course.

Course Link Click Here

Tips for Learning Machine Learning

Practice Regularly: The best way to learn machine learning is through hands-on practice.

Join Online Communities: Connect with other learners and experts on forums and social media.

Work on Personal Projects: Apply your knowledge to real-world problems.

Stay Updated: Machine learning is a rapidly evolving field. Keep up with the latest trends and advancements.

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