Would you like to harness the power of machine learning?
By learning machine learning, you’ll gain the expertise to create machine learning algorithms. One way you could use such models is to automate repetitive tasks that may be clogging up your workflow, and diverting resources from other important processes.
However, to tap into the full potential of machine learning, it’s important to choose the best machine learning courses online.
These courses will help you avoid ML design pitfalls, like overfitting algorithms, and you’ll therefore create reliable models.
Moreover, with this knowledge, you don’t need to create a task-specific algorithm every time you want to solve a problem. Machine learning models can save you the time and trouble of explicit programming, thanks to the ability to adapt to unseen data sets.
In this article, I’ll take you through the best machine learning courses to learn online in 2023 for creating high-performing machine learning algorithms.
Let’s get started.
Python and R are two of the most popular programming languages for data science, and this course teaches you both.
Other course expectations include:
- Advanced coverage of dimensionality reduction. This will help to avoid overfitted ML models that perform poorly when fed with real data.
- Bonus content on natural language processing and other AI aspects. Consequently, this is the best machine learning course online to learn about other important artificial intelligence topics as well.
- Great tips on choosing an optimal and efficient ML model depending on the complexity of the problem you’d like to solve, and other factors.
However, given that each lesson is discussed twice in Python & R, you may feel that the course contains duplicate content. On the plus side, you get to learn both Python and R, so it is one of the best machine learning courses on Udemy in terms of versatility.
This machine learning course by Stanford University will take you behind the technology fueling self-driving cars, and how you can put this knowledge to work for you.
Course highlights include:
- Practical assignments on using GNU Octave, which enables you to better understand the theory through visualization of the results.
- A simple approach to complex linear algebra and calculus behind machine learning algorithms. It is for this reason that this is the best machine learning course on Coursera with regard to teaching style.
- Important regularization techniques, to ensure any models you build don’t overfit the training data and therefore perform accurately.
Some of the assignments feel a little spoon-fed as they lack complexity and leave little coding to the learner. The upside to this is that it makes it an excellent introductory tutorial, especially if your recollection of calculus is a little rusty.
What are the challenges involved in creating ML models?
This is one of the best machine learning courses on LinkedIn Learning to get ahead of common ML pitfalls and create reliable models for various tasks.
Ultimately, you’ll be able to:
- Get 2.8 CPEs to help you make progress if you’re studying information technology or would like to take the degree.
- Create decision trees for more effective data mining techniques to develop reliable supervised ML models.
- Use the Naive Bayes to perform multi-class classification, which can help you create algorithms with better accuracy in making real-time predictions.
The instructor is largely unavailable for responses, so you may have to get technical assistance elsewhere. It is still nonetheless among the best machine learning courses online because it also addresses common ML problems you may encounter from the onset.
This exhaustive Bootcamp covers Pandas, Tensor, and Maptlotib, among many other machine learning tools and resources.
Some course highlights include:
- A witty and engaging teaching style that doesn’t overwhelm beginners with the complex math behind ML models, rather only summarizes succinct working concepts. It is therefore the best machine learning course online for beginners.
- Learning how to work with Amazon Web Services and Spark for simple and effective big data analytics.
- A Python crash course so you don’t need to bring any prior coding experience, making this the best machine learning course on Udemy for non-programmers.
Given that the course is a little old, you may have to find alternative approaches with deprecated features in new libraries. The Q&A will be of great help in this regard so this shouldn’t be too much of a problem.
If you’d like to build machine learning skills in readiness for a job, then getting a professional IBM machine learning certification is a wise idea.
By the end of the course, you’ll be able to:
- Retrieve data from the Cloud, NoSQL, and SQL databases, among a variety of other data sources.
- Work with a huge range of tools like Watson Studio and Jupyter Notebooks. This is therefore the best machine learning course on Coursera for hands-on labs.
- Differentiate supervised, unsupervised, and reinforcement learning and understand when to apply each to solve specific problems.
Simulated data is used quite often across the 6 courses in this specialization. However, it is still one of the best machine learning courses online as the data sets offer excellent data analytics skills you can use no matter the nature of data you’ll work within real-life.
With a heavy focus on applied learning, this is the course that’ll show you how to handle non-ideal data situations when creating ML models.
The course covers.
- Exploratory data analysis techniques on how to work with dirty data to create algorithms that are still reliable in spite of this.
- Best practices for categorical and continuous data cleaning, which will help you to avoid underfitting and overfitting your machine learning algorithms.
- How to evaluate a model so that it generalizes to unseen examples. This is therefore the best machine learning course on LinkedIn Learning for creating reliable algorithms.
While the course doesn’t concentrate on building any specific machine learning algorithms, it gives you the knowledge and tools to derive a generalized ML model on your own that can solve a wider range of problems.
With a little Python background, this training takes machine learning to the next level.
At the end of this course, you’ll be able to:
- Upscale your machine learning models to a massive enterprise-level using Apache Spark. As a result, this is the best machine learning course online for big data business applications.
- Comfortably use Keras and Tensor flow to build artificial neural networks, such as a movie recommender, that can recognize patterns and adapt to execute tasks.
- Learn about generative adversarial networks for effective training, thanks to extra content included in the new 2023 update. This is therefore one of the best machine learning courses on Udemy for learning relevant and modern ML topics.
The only downside to this course is the shortage of assignments to evaluate student performance. Regardless, the instructor is evidently a subject matter expert and connects his professional experience with practical examples that you can use for self-evaluation.
The University of Washington offers this hands-on, 4-part specialization to help you break into a career around machine learning or to simply improve your business processes.
The course covers:
- Several real-life case studies to guide you through important topics, making this one of the best machine learning courses on Coursera for applied learning.
- The analysis of complex and large data sets, just as you’d encounter in the real world, and how to build intelligent applications to make predictions using this data.
- An approach to creating programs to solve common business challenges, making this one of the best machine learning courses online for business owners as well.
When it comes to course support, you may not find timely help from the instructors given it was last updated a while ago. That said, the specialization is well-put-together and has plenty of additional material you can rely on for guidance.
TensorFlow is an excellent software for training deep neural networks.
If you’ve always wanted to learn TensorFlow, this course offers the right training.
Some course benefits include:
- As far as building a model from installation to deployment goes, this is the best machine learning course online to master the entire process, including cloud and local deployment.
- Important data visualization tips for TensorBoard to better understand your machine learning models.
- Pointers on getting started with TensorFlow for both macOS and Windows, making this one of the best machine learning courses on LinkedIn Learning for cross-platform expertise.
However, the course uses an older version of TensorFlow so you may run into a few challenges during setup. The good news is that students have since addressed and fixed these issues in the forum, so you’ll get the help you need to quickly get up and running.
If you specifically want to learn machine learning via R programming, this may be the course for you.
Some course highlights entail:
- Web scraping techniques, which enable you to extract data from various source formats such as SQL files, Excel spreadsheets, and CSV files.
- There are many resources for offline study, so this is one of the best machine learning courses online. Moreover, there’s a generous number of exercises as well to put your newly-gained knowledge to the test.
- Creating interactive visualizations using ggplot2 and Plotly to give instant data conclusions.
Notably, the sections on cross-validation feel a little rushed as they aren’t covered in great detail. Even so, it remains the best machine learning course on Udemy for R. That’s because there’s enough practical guidance in the section to help you gauge the accuracy of your ML models.
What mathematical knowledge is needed to learn machine learning?
This 3-part specialization covers the prerequisite math necessary to launch a data science career and better understand ML concepts.
The course covers:
- Principal Component Analysis (PCA), and how you can derive this dimensionality reduction technique to compress raw data for your algorithms.
- Linear Algebra, and in particular, how matrices and vectors influence machine learning models, and how you can better work with them to manipulate datasets.
- Multivariate calculus techniques for dealing with design situations involving several variables. Moreover, you also get tool recommendations to make calculus faster and easier.
The downside is that this specialization lacks practical content on creating ML algorithms so you’ll not be actually creating any models. However, it is one of the best machine learning courses on Coursera to get the mathematical foundation needed to thrive in advanced machine learning and data science classes.
Would you like to know how to clean big data?
This natural language processing (NLP) course offers the knowledge you need to process and analyze unstructured text data.
Course highlights include:
- You also get 7.2. CPEs, making this the best machine learning course online if you’re interested in studying Information Technology.
- Advanced techniques for vectorization and cleaning data so you can feed your models quality data, and you’ll also get to build two ML models.
- Learning how to use one of the most powerful NLP libraries, namely NLTK, to explore data sets and carry out pre-data processing.
Some experience with Python is required to take this course so it may not be beginner-friendly. On other hand, it is one of the best machine learning courses on LinkedIn Learning for intermediate Python learners looking to enhance data processing with NLP.
Implementing machine learning algorithms can be challenging.
That’s where this course comes in to give you tips for successful machine learning modeling.
You’ll also get to learn about:
- What computer science and big data are, and how dirty data, in particular, affects the quality of your models.
- The top five tools that data scientists use, including pointers on how to get started with these resources. This is therefore one of the best machine learning courses on Udemy for data scientists.
- Python in a bonus crash course where you’ll build a model that predicts survivability on the Titanic.
Prior exposure to Python is necessary to understand the coding examples, some of which are explained superficially. However, you get a Python crash course at the end to smooth it all over so it’s still one of the best machine learning courses online for data science.
Machine learning is not only a concept for data scientists but even if you’d just like to solve everyday problems.
Some course highlights include:
- In terms of teaching style, this is one of the best machine learning courses on Coursera as it breaks down complex topics for both technical and non-technical learners.
- Lessons on how to obtain reliable data sets and testing best practices for the ML models you build.
- Because you get to train a machine learning model on a non-programming platform, this is the best machine learning course online if you don’t have any coding experience.
Given that the complexity has been dialed down to accommodate everyone, you may find this course somewhat basic if you’re keen on data analytics. However, even if you’re an advanced learner, you also get new insight into the future of machine learning.
For a quick overview of strong and weak AI, this is the best machine learning course on LinkedIn Learning to get you started.
You’ll learn about:
- Integrating artificial intelligence, namely deep learning, with big data and other technology to take over cumbersome and repetitive tasks and make them more efficient.
- The concept of artificial neural networks, and the internet of things, and how to create models that learn and solve problems.
- Common challenges that arise when programming machine learning models. Consequently, this is one of the best machine learning courses online for troubleshooting.
Unfortunately, you’ll not get to build any ML programs in this course. Nevertheless, you’ll take a look at existing AI models and the underlying machine learning concepts powering how they work. So you’ll still get tons of practical knowledge.
Are you ready to start your machine learning journey today?
All of the tutorials that have made it onto this review of the best machine learning courses to learn in 2023 are an excellent choice.
If you’re comfortable simultaneously learning two programming languages popularly used for machine learning, I recommend the Machine Learning A-Z TM: Hands-On Python & R In Data Science course on Udemy.
In this tutorial, you also gain important programming knowledge for data science.
On the flip side, the Supervised Machine Learning course on Coursera is a great match if you’d prefer to learn coding from a single approach for now.
Whatever choices you make, all of these machine learning courses will help you build reliable algorithms to add to your resume or simply improve your workflow.