Machine learning (ML) is a sub-domain of artificial intelligence (AI), and is an increasingly popular concept today. Today’s media is rife with stories about AI and ML and their impact on how we work, live, play, and shop. So naturally, anyone wanting an IT-related career and wanting to stay ahead of the tech curve needs to keep up with the technology. You do this by taking courses and learning.
However, learning can get expensive. That’s why this article spotlights free machine learning courses that you can take and increase your knowledge base while better understanding a growing technology.
So, let’s explore how you can learn about machine learning in a way the only cost to you is your time.
What is Machine Learning, and Is It Difficult to Learn?
Machine learning is a classification of artificial intelligence, an advanced field that covers diverse aspects of computer science, mathematics, and coding. Machine learning imitates how humans learn. It’s a computer science division that uses data and algorithms to correct and modify its actions as it acquires more information.
Here are some examples of machine learning that you encounter every day:
- Virtual assistants. Siri, Alexa, Google Home, Amazon Echo, Samsung Bixby, etc.
- Social media. Facial recognition, people you may know.
- Email spam filtering. Spam filtering techniques keep up with the latest scams.
- Online customer support. Chatbots handle customer service queries.
- Search engine result refining. Algorithms learn to improve your results based on your activity.
Machine learning relies on complex tools such as advanced mathematics, algorithms, statistics, programming, and software engineering. If you have an educational background in mathematics, statistics, or computer science, you are already well on your way.
However, if you didn’t go to college or your major isn’t related to AI or ML (e.g., Journalism, Geology, Star Wars Trivia), don’t despair. There are plenty of resources available to help you catch up. Just bear in mind the journey will be more challenging than it is for people with the right educational background.
Also Read: AI ML Engineer Salary – What You Can Expect
So, Can You Learn ML for Free?
The good news is, yes. Yes, you can. There are a ton of free machine learning courses available. In fact, one of your biggest challenges in getting machine learning skills might be choosing the right ones from such a wide range of options!
There are even options for taking a machine learning course free with a certificate. Many organizations prefer talking to candidates with certifications in the skills and disciplines they seek. Certification courses teach you the valuable knowledge you need and provide you with tangible, verifiable credentials showing you can do the job.
Since machine learning has many facets, let’s break down the field into subdomains and examine the different free machine learning courses.
Exploring Free Machine Learning Courses
- Machine Learning Basics. It would be best to start with the fundamentals regardless of what you’re trying to learn. Once you grasp the basics, you build a firm foundation to construct an increasingly complex knowledge base. A course in machine learning basics gives you that solid foundation and skills vital for helping machine learning engineers, AI professionals, and data scientists. Introductory courses provide hands-on experience in essential areas like supervised and unsupervised learning, data preprocessing, time series modeling, and text mining. This course benefits machine learning engineers, AI engineers, data scientists, and data analysts.
- Introduction to Artificial Intelligence. As we previously said, machine learning is a subset of artificial intelligence, so getting familiar with the subject makes sense. Intro to AI courses provide an overview of artificial intelligence concepts, workflows, machine learning, and deep learning basics. Additionally, this is an excellent course if you want to lean more toward an AI-related career over an ML-related career.
- Deep Learning for Beginners. Here’s another introductory course that covers fundamentals. This time, it’s focusing on deep learning. Beginner deep learning courses explore the basics such as TensorFlow, deep learning frameworks, recurrent neural networks in Python, convolutional neural networks, and various deep learning applications.
- Introduction to Machine Learning Algorithms. Artificial intelligence and machine learning depend a lot on algorithms. You could even say that algorithms are the building blocks for these innovative technologies. Algorithm introductory courses provide solid training in supervised learning and unsupervised learning algorithms, PCA, k-means clustering, reinforcement learning, and Q-learning. These courses teach you how machine learning algorithms work and how to apply them in data analysis and automation. This type of course opens the door for career opportunities such as data scientist, machine learning engineer, artificial intelligence engineer, NLP scientist, and more.
- Introduction to Neural Networks. Neural networks are a sub-domain of machine learning and comprise computer systems modeled on the human brain and nervous system. Introductory neural network courses cover the basics like convolution neural networks, data processing by neurons, gradient descent algorithms, backpropagation, and recurrent neural networks. Careers such as neural network engineers, data analysts, and NLP scientists rely on neural network knowledge.
- TensorFlow for Beginners. TensorFlow figures heavily in the machine learning field. It’s an open-source, end-to-end deep learning framework favored by machine learning engineers, data scientists, business intelligence developers, and NLP scientists. This course teaches TensorFlow basics, installation, TensorFlow object detection API, and object identification in images and videos.
- Introduction to Machine Learning with R. R is a programming language used for statistical computing, graphics, and machine learning. This course covers machine learning basics, algorithms, logistic regression, decision trees, linear regression, random forest, SVM, hierarchical clustering techniques, and various applications. These programs also cover R programming in detail. This course is ideal for ML engineers, analytics managers, business analysts, and information architects.
- Image Recognition Basics for Beginners. This introductory course teaches image processing and image recognition techniques for object detection. The course provides training on processing data from image files, classifying image types, and working with different neural networks. Image recognition basics are best suited to machine learning engineers, business intelligence developers, data scientists, and NLP scientists.
- Introduction to Supervised and Unsupervised Machine Learning. Machine learning is broken down into supervised and unsupervised forms. As the names imply, supervised learning uses a teacher to train a machine with labeled data, while unsupervised learning lets the algorithms act without guidance or classified/labeled information. Introductory courses in these subjects typically explore different classification and regression techniques. These courses cover topics like decision trees and clustering methods while providing a comprehensive understanding of supervised and unsupervised learning techniques. This course particularly benefits ML engineers, business analysts, analytics managers, and information architects.
- ChatGPT/Generative AI. You’ve probably heard much about this topic lately, including some controversies. Generative AI is a broad artificial intelligence field covering techniques and models to create new content, including images, music, and text. Generative AI can learn patterns from existing data and use that information to generate original content that conforms to the learned patterns. ChatGPT, conversely, is a specific implementation of generative AI. ChatGPT was explicitly designed for conversational purposes, a language model trained on extensive amounts of text data. This training enables it to generate human-like responses to user prompts. So, just like machine learning is a sub-genre of artificial intelligence, ChatGPT is a sub-genre of generative AI.This subject is a new, complex technology prone to many misunderstandings, so it’s wise to learn the basics. Knowledge cures misinformation. Fortunately, free ChatGPT/Generative AI courses are available to help train professionals and eliminate misconceptions. This course covers natural language processing (NLP), text classification, language translation, and how it all relates to machine learning. Introductory courses like this one explain what ChatGPT is, how it works, and examples of how it functions.
Also Read: What are Today’s Top Ten AI Technologies?
How Do You Get Your Machine Learning Journey Started?
Now that you know the kinds of free machine learning courses you need, let’s combine this into a coherent learning plan.
- Learn how to program. Programming is the lifeblood of machine learning. If you don’t know Python, learn it; it is the most popular language machine learning professionals use.
- Learn math. If you don’t have an educational background that includes mathematics, learn the subject. This training includes subjects like calculus, algebra, and statistics.
- Familiarize yourself with data visualization libraries. Data visualization libraries such as Numpy, Pandas, Matplotlib, and Scipy help you analyze all kinds of data, manipulate it, and see it graphically. These libraries were designed for Python.
- Take relevant courses. And this brings us back to the meat of our article! Start taking some free machine learning courses and bulk up your skill set.
After you master these steps, the future steps are up to you! Will you look for a position or wait a bit and create some practice projects? It’s your decision!
How to Learn More About Machine Learning
If you take any of the above free machine learning courses, you may discover you have a genuine passion and aptitude for machine learning. If so, you can take your machine learning education to the next level with this intense six-month machine learning course.
This online bootcamp is a comprehensive artificial intelligence and machine learning course that provides you with a high-engagement learning experience, including live online classes and over two dozen hands-on projects with integrated labs. The course covers ChatGPT, computer vision, deep learning, ensemble methods, explainable AI, generative AI, and more.
According to Glassdoor.com, machine learning engineers in the United States make an annual average of $151,952. Start taking advantage of these fantastic machine learning resources and create new career opportunities for yourself. Sign up today and start your journey to a better future.