Free Online Tutorials to Help You Develop Machine Learning Applications

The machine learning (ML) and data science space have always been interesting, particularly because of the potential of ML algorithms and models. The recent Artificial Intelligence (AI) boom has also contributed to this and made it much more exciting, leading to a 23% growth rate for machine learning engineers since 2022.

However, as exciting as the space can be, breaking into it can be quite challenging for beginners and even more challenging for those with some experience who want to build machine learning applications. The problem here isn’t a lack of resources to help you get started; it's finding free courses or tutorials that cover foundational topics like mathematics and data analysis, and advanced concepts like Machine Learning Operations (MLOps).

But how can you find free online learning resources to help you build machine learning applications? Is there anything you should look at when picking machine learning resources? This blog will introduce you to 10 free machine learning tutorials and platforms and highlight factors to consider when picking free online learning resources to help you start with ML models and application building.

10 free machine learning courses and tutorials to help you develop real-world apps

Based on some criteria like course content, style, instructor expertise, and skill level, here are ten free machine-learning tutorials and platforms to help you learn machine learning and build real apps:

• Guide to Machine Learning System Design and Best Practices by Jozu Learning
• WorldQuant University’s Applied Data Science Lab
• freeCodeCamp’s Machine Learning with Python
• Kaggle Learn
• YouTube channels like Python Simplified by Mariya and Simplilearn Machine Learning.
• Fundamentals of Reinforcement Learning by Alberta Machine Intelligence Institute
• Supervised Machine Learning: Regression and Classification by DeepLearning AI and Stanford University
• Machine Learning courses on Udemy such as Python Mastery: Real-World Machine Learning Projects.
• Machine Learning Application Courses on Alison like Machine Learning for Apps and Building a Machine Learning App Interface by YouAccel Training.
• Google’s Machine Learning and Artificial Intelligence Learning Path

1. Guide to Machine Learning System Design and Best Practices by Jozu Learning

As much as model building and training are essential, following best practices when designing your ML application will ensure you have scalable projects that can drive impact. The Jozu Learning guide on machine learning system design and best practices sets you up for your machine learning engineer career by teaching you to build reliable, scalable, and maintainable solutions. The course starts by showing you how to set up your ML development environment for data preprocessing, MLOps, feature engineering, testing, training models, version control, and deployment for production. Thanks to its comprehensive approach, the Jozu Machine Learning course is a must-take for everyone.

The course also offers hands-on projects, covers system design, and provides tips on optimizing ML workflow and avoiding common bottlenecks. A key project in this course involves designing and implementing workflow management for machine learning applications with Apache Airflow. Another great spot to learn for free within Jozu Learning is the Jozu blog, home to various rich and valuable machine learning tutorials.

In addition to the learning hub, Jozu has an open source AI/ML development hub focused on improving KitOps, an MLOps tool designed for packaging and versioning AI/MI models into ModelKit. Community developers currently manage this development hub. You can learn more about the KitOps solution by following this quick start guide.

1. WorldQuant University’s Applied Data Science Lab

WorldQuant University is a U.S.-based and Distance Education Accrediting Commission (DEAC) accredited university learning platform that offers learners data-inclined online courses.

Although not all courses are free, the Applied Data Science Lab course is available at no cost. This course is offered online and runs on a continuous education module, allowing you to begin learning as soon as your application is accepted and you pass the admission quiz.

The Applied Data Science Lab program is primarily aimed at beginners, as the curriculum starts with beginner-level Python and requires no prior knowledge of data science and machine learning. The curriculum covers various topics, from data wrangling and analysis to building multiple models, such as a regression model that predicts real estate prices. The course format and style are text and video-based, and there are hands-on assignments such as customer segmentation and A/B testing projects to solidify key concepts.

1. freeCodeCamp’s Machine Learning with Python

freeCodeCamp is one platform everyone in tech, from software developers to analysts and machine learning engineers, has probably heard of. This non-profit educational platform prides itself on being a friendly community that has taught various busy individuals how to code and successfully transition into tech for free.

To become an ML engineer, you should take the freeCodeCamp’s Machine Learning with Python course. This course covers a wide range of topics, starting with the fundamentals of machine learning and extending to in-depth explorations of ML algorithms and models. It goes beyond the fundamentals and dives into neural networks using TensorFlow, natural language processing with Recurring Neural Networks (RNNs), and reinforcement learning with Q-Learning.

Most importantly, the instructors end this course with quizzes and hands-on projects, such as building a book recommendation system engine using the K-Nearest Neighbor (KNN) algorithm to softly introduce you to the world of building real ML apps.

1. Kaggle Learn

Iconically, the Kaggle platform is known as the home of data science and machine learning datasets. Everyone, from ML engineers to developers, explores these datasets for learning and building ML projects. Besides the datasets, they also host machine learning competitions to help you grow your data science skills applicable to various industries.

Kaggle Learn offers short, comprehensive mini-courses you can complete quickly. The learning courses, authored by the Kaggle community, aim to simplify complex topics through tutorials and exercises to get you started in machine learning. The Kaggle Learn platform is also organized sequentially to give you a structured learning curriculum. This curriculum covers data cleaning, machine learning, deep learning, AI ethics, SQL, feature engineering, time series, and data visualization. Most importantly, it ends with a short course on machine learning explainability and an introductory course on Game ML that covers how you can build video game bots.

1. YouTube

One of YouTube’s earlier taglines when it first launched was “Broadcast Yourself.” Since then, YouTube’s creators have created and shared content catering to the platform’s diverse communities, including the machine learning community.

Besides the Ads, the platform is free for everyone. You just need to find the right content and watch. However, unlike the other platforms on our list, learning via YouTube can be unstructured, as you often need to find the next topic on your own. Finding content can be a lot of work, especially for beginners who can’t find a good playlist. Another issue is that you might be watching an outdated tutorial. However, for non-beginners who know their way around the platform, YouTube is a gold mine waiting to be explored.

You can explore some playlists, such as Python Simplified by Mariya, Simplilearn Machine Learning, and Machine Learning for Everybody.

1. Fundamentals of Reinforcement Learning by Alberta Machine Intelligence Institute

The Fundamentals of Reinforcement Learning course focuses more on reinforcement learning, a subset of machine learning that teaches software how to make decisions and learn. Reinforcement learning is applied in various real-world applications, from autonomous cars and gaming to the proximal policy optimization algorithm, which ChatGPT uses.

The instructors cover everything from basic exploration methods to Markov Decision processes and dynamic programming for business and industrial control problems. Like the other courses mentioned, it includes hands-on programming assignments and quizzes.

1. Supervised Machine Learning: Regression and Classification by DeepLearning.AI and Stanford University

The Supervised Machine Learning course is pretty beginner-friendly. The instructors focus solely on the regression and classification of machine learning models. Thus, the course will teach students everything they need to know about building and training various supervised models in Python and its libraries.

These models are important as they are used outside the classroom for various tasks, such as detecting spam emails, predicting stock prices, and diagnosing medical conditions.

1. Machine Learning courses on Udemy

Udemy is an online learning platform with over 70 million learners that offers over 20,000 courses on various topics. While the platform hosts paid courses, free Machine Learning courses on Udemy are also available. These courses are highly rated, and a few include hands-on projects like predicting if a patient has cancer and building a recommendation system. A personal recommendation will be Python Mastery: Real-World Machine Learning Projects.

In addition to the courses, this platform offers free niche-specific and short 10-minute lessons that simplify complex topics in machine learning.

1. Machine Learning Application Courses on Alison

Similar to Udemy, Alison is a learning management system (LMS) that offers users free access to various industry experts and 5,000+ CPD UK-accredited courses. The platform categorizes its tutorials into certificate courses, which are shorter and more focused, and diploma courses, which are longer and offer a more in-depth understanding of the subject. In terms of style, the learning platform includes modules and assessments that learners can attempt multiple times.

You should explore the free Machine Learning Application Courses on Alison and start building. Personally, you need to explore Machine Learning for Apps and Building a Machine Learning App Interface.

1. Google’s Machine Learning and Artificial Intelligence Learning Path

Google’s ML and AI learning course, created by the Google research team, skips the basics to the more advanced topics in the machine learning field. Unlike the other courses, it allows you to choose from different learning paths, such as Generative AI Training, Data Scientist or ML Engineer, or Contact Center Engineer. Each path is tailored to be more specific to a particular topic or career, featuring designated subjects under each category.

This course covers everything from Big data, Explainable AI, and ML on Google Cloud to TensorFlow operations, using LLMs to predict outcomes, MLOps, ML Pipelines, and deploying machine learning applications on Vertex AI.

Now, let’s look at how and why you should check out these tutorials.

Picking the right machine-learning tutorial to help you develop real-world apps

Machine learning plays a significant role across various industries, from its use in cybersecurity and fraud detection to product recommendation and speech recognition software. According to Rackspace Technology's 2024 IT outlook report, it will continue to be valued by organizations. The same report also cited finding skilled ML and AI talent as one of the challenges organizations face when implementing these initiatives in the workplace. Thus, there is no better time to learn about machine learning. However, to start a career in ML, you need to find the right tutorial.

So, what should you consider when selecting ML tutorials? Well, for starters, you should consider these four factors:

• Your learning goal and current skill level
• Course content format and style
• Instructor expertise and recognition
• Cost and financial options

Your learning goal and current skill level
The whole point of taking a course is to learn a new skill or upgrade your current skills. However, regardless of your reason, you should start by self-evaluating your current skills and what you hope to achieve.

So ask yourself, what are you trying to achieve? Do you want beginner-friendly machine learning courses that would help you transition into an ML role or a course that would give you a deep understanding of ML algorithms and how to build ML applications?

In simple terms, defining your learning goals and assessing your skill level will help you choose courses that are most beneficial for you.

Course content format and style
Once you have a goal, you must pick a course—possibly a couple from the ten machine learning courses mentioned earlier.
But what is the content, format, and style of the course? Some questions to ask yourself include:

• Does the course include capstone projects, case studies, or assignments? These will help reinforce your learning.
• What does the curriculum cover—the fundamentals, theories, mathematics deep drive, model building, MLOps, optimization, or ML applications building?
• Is it an article, text-based, video-based course, or a combination of the above?
• Are there live sessions, lecture notes, forums, or a community platform—like Slack, Telegram, Discord, or WhatsApp? These will provide you with community support and accountability.

These questions impact your learning experience in terms of your preferred learning style, commitment, and the educational values of the machine learning tutorial.

Instructor expertise and recognition
The tutorial should be taught by someone or an organization that understands the subject. This is important because only an experienced instructor can communicate and pass on the correct knowledge.

Recognition, on the other hand, depends on the kind of content. While articles won't provide badges, they offer specific insights that can help you resolve issues and bugs. In contrast, courses from a reputable organization often award badges and certifications. Although certifications may not always hold significant value, they can boost your resume and demonstrate expertise to potential employers. Alongside badges and certifications (when applicable), it’s also important to consider testimonies, ratings, and feedback from previous readers, participants, or students.

Cost and financial options
Ultimately, cost is a significant factor, and quality tutorials can often be pricey. Therefore, it's advisable to start with the free and comprehensive tutorials and courses mentioned earlier.

What’s next for you?

As you’ve seen, you don’t need to break the bank to get into machine learning. These platforms, programs, and sites offer free and quality machine learning courses that help learners build ML/AI models and applications. So, pick a course and commit to it.

However, once you've learned the basics, don’t stay too long at the beginner’s end; explore in-depth and specialized topics like MLOps and ML application deployment. For example, you can transform your ML skills and knowledge by building, sharing, and running your project anywhere with KitOps.

Like DJ Patil, the first US Chief Data Scientist, said, “The best way to learn data science is to do data science." Start doing data science by taking the Jozu ML course and build alongside other ML engineers in our Discord community with KitOps today!