The market for machine learning is expected to grow at a rate of about 40% in just five years, to reach $90.1 billion by 2026. This shows how companies are becoming more interested in investing in solutions that use machine learning and their need for skilled professionals to develop custom software. With these encouraging statistics, it’s quite clear why so many people are keen to explore the field of artificial intelligence and machine learning to gain the skills they need to work as machine learning engineers. If so, here’s a list of recommended free courses to get you started in the right direction. While there are many free AI & ML courses available, we’ll walk you through the best Free AI & ML courses, so you can start your education with assurance. Let’s explore your options.
What is AI (Artificial intelligence)?
Artificial intelligence, or simply AI, is a way of giving computers a hint of human intellect. In other words, we are teaching machines to learn from their mistakes, adjust to what they observe, and make judgments in the same way that humans do. Artificial intelligence (AI) is capable of many incredible things, such as the ability to identify drawings we make, understand the world around us, solve complex puzzles, and sometimes even generate original ideas.
However, what’s fascinating about AI is that it goes beyond complex algorithms such as ChatGPT or StableDiffusion. The world as a whole is vast and open! So, does AI. Overall artificial intelligence (AI) is a very interesting field that has many applications, including marketing, virtual world creation, medical, and much more!
Why Should You Learn AI?
The demand for experts in artificial intelligence is rising quickly as AI technology becomes more and more popular among governments and corporations. AI-experts and other tech professionals related to this field are in high demand, according to LinkedIn. The Future of Jobs Report from the World Economic Forum has also highlighted it as the future. And if you’re concerned about finances In the United States, the average salary for a machine learning engineer in the USA is $154,564 per year, or $74.31 per hour. The average annual salary for entry-level occupations is $124,966, but the majority of professionals can earn up to $200,000.
Even if you’re not interested in working in AI, you should still learn the topic in general. It’s likely that you’ve heard of AI producing art and sparking copyright disputes, or even of AI systems like ChatGPT being charged with plagiarism in educational settings. Understanding AI makes it easier to have conversations about technology, even if you’re not a tech wiz. In 2023, AI is more relatable than ever.
List of Free AI & ML Courses on Python with Certificate
Machine Learning with Python: A Practical Introduction
According to GitHub, Python is the most popular programming language for machine learning. So, knowing how to use Python for machine learning is a terrific idea, and this machine learning with python course will assist you in doing so.
Dr. Saeed Aghabozorgi, a Senior Data Scientist at IBM, will guide you through five easy-to-follow modules for free:
An Introduction to Machine Learning: Discover the many forms of machine learning, their applications, and the Python libraries that should be used.
Regression: Examine several prediction techniques, such as linear and non-linear models, and learn how to assess them.
Classification: See how well your models perform by learning how to classify objects using techniques like decision trees and K-Nearest Neighbor.
Unsupervised Learning: Learn about clustering, which is the process of assembling comparable objects using a variety of techniques, including K-Means.
Recommender Systems: Learn how recommendation systems operate and how they might make suggestions for items you might find interesting.
After completing it, you are going to understand various algorithms, become aware of the fundamentals of machine learning, and even be able to apply what you have learned. It’s a fantastic place to start!
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Check out the TensorFlow course taught by Laurence Moroney! It’s an excellent free option if you want to start creating interesting machine learning models.
This course will teach you:
- How to use TensorFlow’s greatest features.
- How to create your neural network by learning
- How to identify objects in images.
- To further improve the functionality of your network, apply convolutional neural networks.
You don’t have to be an expert in ML or DL, but you should know the basics of Python and high school math. With nearly 40,000 reviews, it has an amazing 96% rating. So, it is unquestionably worth looking into!
Fundamentals of Reinforcement Learning
This free ML course on reinforcement learning is offered by Matha White and Adam White. Reinforcement learning is used in various applications such as marketing, games, healthcare, and self-driving cars.
There are four sections of this ML course. You’ll have the opportunity to work on practical projects and complete quizzes that will allow you to apply your knowledge to address actual business issues.
- Determine how to investigate and decide most effectively.
- Utilize dynamic programming to address issues
- Learning how to program dynamically
Try this ML course to advance your skills if you’re comfortable with probability and making predictions, have a basic understanding of linear algebra and calculus, and can work with Python 3.0 and algorithms!
IBM Applied AI Professional Certificate (via Coursera)
Check out Coursera’s IBM Applied AI Professional Certificate! You can learn some extremely fascinating things from this beginner-friendly course, including:
Learn about AI: Understand terminologies like machine learning, deep learning, and neural networks, as well as the definition and applications of artificial intelligence.
Build AI Tools: Using Python, IBM Watson AI services, and APIs, you can build AI-powered tools even if you’re not a coder.
Make Virtual Assistants: Easily create chatbots and virtual assistants without the need for programming knowledge. These may be integrated into websites.
Computer Vision: Python, OpenCV, and Watson can be used to learn about computer vision. In the cloud, you will create personalized picture classifiers.
Regardless of your interest in technology, anyone can enroll in this AI course. Don’t worry if you’ve never programmed before; they even provide a basic Python courses to get you started. Thus, it’s a great way to get started with AI!
AI for Everyone by Andrew Ng (Coursera)
Previously, Andrew Ng, one of the founders of Coursera, offered an amazing free ML course. It had been taken by over 5 million individuals! However, it has now evolved into a specialization in machine learning, for which you can receive a certificate.
The good news is that, although the specialization isn’t free, you can audit three of its courses for free on Coursera. This implies that while you are able to see the classes without charge, you will not be able to complete any activities, quizzes, or certificates.
The three free Machine Learning courses are as follows:
- Regression and Classification in Supervised Machine Learning
- More Complex Learning Algorithms
- Unsupervised Education and Reinforcement Education
Andrew Ng is an excellent teacher. He is an expert in artificial intelligence, machine learning, and deep learning. He has produced some of the top Machine Learning courses available on Coursera. His instruction is really clear. I heartily suggest these courses if you’re interested in learning machine learning.
You can also enroll in Coursera Plus, if you can afford it, and earn a certificate upon finishing the entire specialization. Win-win situation!
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Practical Machine Learning with Scikit-Learn [Free Course in Udemy]
A well-liked Python machine learning library, Scikit-Learn is excellent for novices. It provides you with a simple approach to work with machine learning models and understand the entire procedure.
Not only will you study the fundamentals of machine learning in this course—such as what a target variable or feature is—but you’ll also learn how to use Scikit-study in Python to build an entire machine learning model.
What you’ll be able to do is:
- Putting techniques for boosting, classification, and regression into practice.
- Determining the optimal methods for various datasets.
- Getting your data ready for machine intelligence.
From importing and cleaning data to training models, preprocessing, and designing features, you’ll gain practical expertise with every step of machine learning.
To put it briefly, for those who are new to machine learning, this course is ideal for getting started. Once you’ve mastered Scikit-Learn, you may move alone and investigate even more potent libraries like TensorFlow. It’s a fantastic place to start!
Deep Learning Prerequisites: The Numpy Stack in Python V2 [Free]
Here’s another fantastic free Deep Learning course available on Udemy. This one covers four essential Python libraries for deep learning, machine learning, and artificial intelligence (AI): Numpy, Scipy, Pandas, and Matplotlib.
In case you were wondering, Scipy employs building elements like matrices and vectors for particular tasks, whereas Numpy assists with them. Pandas excels at importing data, particularly from databases, while Matplotlib adds fantastic plotting capabilities to your data visualization, including scatter and line charts.
You will become familiar with all of these libraries and learn how to use Scikit-Learn to conduct supervised machine learning tasks, such as regression and classification, using practical examples in less than two hours.
Here’s what you’ll learn:
- Fundamentals in Matplotlib, Scipy, Pandas, and Numpy
- experimenting with tensors, matrices, and vectors
- Using visuals to make data look awesome
- Managing DataFrames expertly
Additionally, you’ll learn how to use Pandas, Matplotlib, Scipy, and Numpy for other numerical tricks. The best thing, though? The ins and outs of several machine learning models, such as Boosting, Random Forests, Linear Regression, and Deep Learning Decision Trees, will be clear to you.
To put it briefly, it’s an excellent free Deep Learning course that introduces you to Deep Learning with Numpy, Scipy, Pandas, and Matplotlib.
Learn Keras: Build 4 Deep Learning Applications
Take a look at this awesome “Learn Keras: Build 4 Deep Learning Applications” free Deep Learning course on Udemy. It ultimately comes down to Keras, a potent Python machine learning library.
Just in case you weren’t aware, Keras is fantastic. It simplifies the process of creating and evaluating deep learning models. With just a few lines of code, you can design and train neural network models thanks to its wrapper around large math libraries like TensorFlow and Theano. That’s really cool.
The main goal of this Deep Learning course is to show you how to use Keras to develop a complete Python machine learning project. Additionally, you will learn how to optimize neural networks and deep learning models.
Here, You will truly understand the workings of the model and the entire procedure because the instructor goes over each line of code. It’s an excellent way to get started with deep learning and Keras!
Final Thoughts
That is everything there is to know about some best free AI & ML courses on artificial intelligence, deep learning, and machine learning in 2024. Acquiring knowledge of these technologies now will provide you with vital abilities and an advantage over others as they are the ones that will rule the globe in the next few years.
Currently, a machine learning specialist can expect to solve some really exciting real-world challenges while earning a nice salary. It’s tremendously satisfying work, so it’s not just about the money!
Thank you for continuing to read. Please remember to forward these free AI, deep learning and machine learning courses to your friends and colleagues if you find them interesting. Feel free to contact me with any queries or suggestions. Our goal is to assist!
