Have you heard the terms like “Artificial Intelligence” and “Machine Learning”? So, at the highest level, AI is defined as leveraging computers or machines to mimic the problem-solving and decision-making capabilities of the human mind.
In this article, I am reviewing the 10 best Machine Learning courses on Udemy. I have picked these courses based on my personal experience and for different types of learners, delving into what makes each course exceptional and providing you with insights to make an informed decision.
Why Pursue a Machine Learning Course on Udemy?
Udemy stands out as a leading online learning platform, providing affordable and accessible education. With a vast library of courses, Udemy offers a range of machine-learning courses catering to different skill levels. The platform’s intuitive interface and the ability to learn at your own pace make it an ideal choice for anyone eager to master Machine Learning techniques.
1. Machine Learning A-Z: AI, Python & R+ ChatGPT Prize [2024]
This course focuses on mastering Machine Learning in Python and R. By the end of the course, students will be able to make precise predictions and build strong Machine Learning models. This course was created by Machine Learning and Data Science experts who want to share their knowledge and expertise with you so that you can learn complex theories, algorithms, and coding libraries.
Who would benefit most from taking this course?
This course is perfect for people who want to understand and use Machine Learning, even if they’ve never coded before. College students thinking about a career in data science, or analysts who want to get better at machine learning for their jobs, will find it very helpful. Also, if you’re running a business and want to make it more valuable by using smart machine-learning tools, this course is for you.
What do I like in the course?
- This course is quite flexible. Each section inside each part is independent. Therefore, you have the flexibility to take up the whole course or simply focus on a specific section and learn what you need.
- The teacher will guide you step by step into the world of Machine Learning. As you move through the course, each lesson will help you build skills and get a better understanding of this exciting area of learning from data.
- I got to work on 5 coding exercises which provided me a lot of hands-on experience and boosted my confidence to take up real-time challenges.
What could have been better?
- The course structure needs to update some minor inconsistencies with versions of software that were made at the time of this course and current versions of software.
- There are difficulties in getting timely responses or support when facing issues or roadblocks within the course. The tutor dedicated an AI to respond but it was not capable of providing the answers.
- Some parts of the course don’t go as deep as they should into why certain tools do what they do. It’s more about showing off what machine learning can do, rather than explaining how it works or why it works that way.
2. Python for Data Science and Machine Learning Bootcamp
This course teaches how to use Python for Data Science and Machine Learning. It is a course for both beginners with basic programming experience and experienced developers who want to learn in-depth how to use NumPy for numerical data, Matplotlib for Python plotting, Seaborn for statistical plots, Pandas for data analysis, Plotly for interactive dynamic visualizations, SciKit-Learn for machine learning tasks, and Spark for big data analysis. Learn how to use Python to analyze data, create beautiful visualizations, and apply powerful machine-learning algorithms.
Who would benefit most from taking this course?
This course is best suited for those who want to become Data Scientists. If you have some programming experience, you can explore the world of Data Science and Machine Learning. According to Glassdoor, the most sought-after and highest-paying job in the United States is that of a Data Scientist, with an average salary of more than $120,000.
What do I like in the course?
- The course provides 100 HD video lectures and detailed code for every lecture.
- At such a low cost, I can take one of Udemy’s most comprehensive courses in Data Science and Machine Learning. Other boot camp courses are usually prohibitively expensive, but this Udemy course is significantly less expensive.
What could have been better?
- The course uses outdated commands. Therefore, there was a major waste of time in downloading new versions. A lot of updates are required for this course
- The course delivery and presentation are less interactive leaving students feeling dissatisfied and resorting to external resources for better understanding.
- The AWS / Pyspark installation instructions are quite difficult to reproduce while the lecture on Pyspark fundamentals itself is too short and uses the smallest dataset of all.
3. Machine Learning and Deep Learning in Python and R
If you want to work full-time in machine learning and deep learning, this comprehensive course is a must. After completing the course, you will be comfortable building predictive Machine Learning and Deep Learning models to solve business problems in R and Python. By the end of the course, you’ll be able to answer subject-specific interview questions as well as participate in data analytics and data science competitions like Kaggle.
Who would benefit most from taking this course?
Every student who completes this course will receive a verifiable certificate. This course will provide a solid foundation, particularly for those who work as business managers or executives and want to apply machine learning concepts to practical problems. You will gain a thorough understanding of fundamental data science and data analysis tools such as Python and R.
What do I like in the course?
- Clear and informative explanations.
- The course helped to understand many machine learning and deep learning fundamentals as well as their implementation.
- The course was comprehensive and well-presented.
- Short videos make the concepts more understandable and fun to learn
What could have been better?
- Some students found it tedious, unintuitive, and riddled with incorrect instructions and horrible accents.
- The course requires more in-depth explanations of many topics.
If you are looking to learn programming languages used in software development, check:
| Best SQL Courses on Udemy | Best Java Courses on Udemy |
4. Learn Machine Learning and AI (Including Hands-on 3 projects)
This Udemy course is ideal for practical learning. Many times, learners become overwhelmed by the technical concepts of AI and machine learning. This course will teach you how to start from scratch and implement various projects. The curriculum includes three projects. This course will improve your skill set, allowing you to advance your career and become a successful machine learning engineer. Throughout the course, you will be able to understand all of the concepts of Machine Learning before moving on to learn about Artificial Intelligence. As a result, one must seize the opportunity to learn everything about the technology that underpins it.
Who would benefit most from taking this course?
This course is great for people who work in IT, like those who create things using Python, who want to learn more about Machine Learning and Artificial Intelligence. It’s also perfect for those already working with Machine Learning or AI and who want to get hands-on experience with real projects.
What do I like in the course?
- The ability and confidence to build end-to-end Machine Learning Projects is the biggest advantage of learning this course.
- Clear and deep understanding of Machine Learning and its application.
What could have been better?
- The concepts are taught at a fast pace therefore missing out on important points like how to install Django, Sublime text, Python, etc.
- The presenter’s lack of polish and professionalism
- Detailed explanation in many sections is required to provide more clarity.
- There are difficulties in getting timely responses or support from the tutor when facing issues or roadblocks within the course. The tutor seems to not take enough time to provide the code but does it himself.
- The curriculum seems outdated and needs to be updated.
5. Machine Learning with JavaScript
Machine learning is the future! There is a common misconception that Machine Learning can only be learned using Python and R. But this isn’t the case. Though Python and R are extremely popular, Machine Learning with JavaScript is easy to learn. With this course, you will learn Machine Learning from the ground up using JavaScript and TensorflowJS, as well as complete hands-on projects. The use of JavaScript for ML opens up new possibilities for the apps that you can create. This course will prepare you for any sub-discipline in the field of machine learning.
Who would benefit most from taking this course?
This course is ideal for professional JavaScript Developers interested in Machine Learning. Many IT companies provide this course as part of their employee skill development. This course is appropriate for learners who have a basic understanding of terminal and command line usage, as well as the ability to read basic math equations.
What do I like in the course?
- The explanations in the lectures were very clear, informative, structured, engaging with clear examples, and excellent delivery in explaining complex topics.
- This course does not use pre-build algorithms and functions but explains the subject matter of algorithms from the basics.
- Provides knowledge to understand the exact math and programming techniques that are used in the most common ML algorithms.
What could have been better?
- The course needs to be updated for some topics. The course uses Tensorflow 1 which needs to be updated.
- More practical methods must be included on how to predict the answers.
6. Machine Learning, Data Science, and Generative AI with Python
With Machine Learning (ML) and Artificial Intelligence (AI) becoming more common, it’s important to have the latest skills. Nowadays, a lot of things are made using AI, and if you’re curious about how big companies like Google, Amazon, and Udemy use AI to make sense of huge amounts of information, this course is right for you. This course has extra lessons and activities about creating AI, using special tools like GPT and ChatGPT, and how AI can pay close attention to data. It’s a full guide with 130 lessons that take 18 hours to watch, and you’ll get to practice with examples in Python.
Who would benefit most from taking this course?
This course can benefit students from a variety of backgrounds. This course can help software developers or programmers improve their skill set or make a career transition to Data Science and Machine Learning. This is also applicable to data analysts or non-technical industries looking to make the transition into the tech industry, and this course will benefit them by moving from tools to coding. The only prerequisite will be basic coding or scripting skills.
What do I like in the course?
- The tutors executed the course with brilliance in going the extra mile to ensure that the tutorials, examples, and skill assessments in their classes worked.
- The course is up-to-date which helps avoid wasting time doing the latest downloads and upgrades.
- The course has a very comprehensive coverage with realistic to-the-point, hands-on examples and exercises.
What could have been better?
- The course could suggest reference books for the different models that have been used in the course for more practice.
- Poor video quality is reported by some users, hindering clear visibility and making it difficult to read the displayed texts.
- Perception of excessive technicality, which may not align with learners from non-technical backgrounds aiming for a more business-focused course.
7. Data Science and Machine Learning Bootcamp with R
A Data Scientist’s job is the most in-demand and highest-paying in the United States, with an average salary of more than $120,000. The multidisciplinary approach of Data Science aids in extracting meaningful insights into business, making it a very rewarding job to understand business problems and solve them using a variety of tools. This course is designed for both beginners with no programming experience and experienced developers who want to switch careers to Data Science. With over 100 HD lectures and detailed code for each lecture, this is Udemy’s most comprehensive and elaborate Data Science and Machine Learning course.
Who would benefit most from taking this course?
This course is useful for anyone interested in becoming a Data Scientist. This course is designed for both beginners with no programming experience and experienced developers who want to switch careers to Data Science.
What do I like in the course?
- This course is best for beginners. The concepts are well explained with clarity. It starts with the basics and scales up to gradually build your skill.
- Lots of exercises and projects are included in this course which helps to hone our newly learned skills and apply them practically.
- The course is non-technical, yet learners secured jobs in data analysis.
- The resources were up-to-date and the instructor’s effort in meticulously preparing all the required notes is highly commendable. All the datasets used are informative.
What could have been better?
- The beginning of the course didn’t explain things well. The lessons, especially the ones about SQL, were too basic and didn’t prepare learners enough. You could tell the teacher was struggling to explain things clearly, which made the course less helpful and not as good as it could have been.
- There are difficulties in getting timely responses or support when facing issues or roadblocks within the course.
8. Machine Learning with Python: Complete Course For Beginners
Machine learning and artificial intelligence are undoubtedly the next big thing in technology. If you believe you are missing out on opportunities and are concerned that your technical skills are insufficient to qualify for jobs in these fields, this course is for you. This course will teach you how to master Machine Learning with Python. It is advantageous to have a Python background. Nonetheless, the experts have designed a curriculum that will allow all non-technical learners to learn the fundamentals of ML before progressing to create ML models, make accurate predictions, and solve problems.
Who would benefit most from taking this course?
Anyone interested in learning about Machine Learning can enroll in this course. This course is best suited for beginner Python developers who are interested in ML and Data Science. Aside from them, students who have completed their school or college and are considering what to do next can start with this short course. Data analysts who want to advance their careers in machine learning can take this course. This course is also useful for people who want to make an impact and add value to their business by implementing powerful machine learning tools.
What do I like in the course?
- The course is explained in plain English and mathematical jargon is not used which makes this course simple and less complex.
- Excellent course with precise and well-explained concepts, useful information and so it is best suited for beginners.
What could have been better?
- There are difficulties in getting timely responses or support when facing issues or roadblocks within the course.
- The way the teacher teaches can be a bit dull and not very lively in some parts. Sometimes, the teacher’s way of speaking was pretty boring.
9. Machine Learning Practical Workout | 8 Real-World Projects
Create eight practical projects and make yourself a hero in deep/machine learning and artificial neural networks from scratch. The possibilities of deep learning and machine learning are exploding. They are being used in a variety of industries, including banking, automotive, transportation, healthcare, and technology. There is a high demand for professionals to implement ML and deep learning concepts in these industries. This course will provide you with a fundamental understanding of deep and machine learning as well as hands-on experience with various techniques.
Who would benefit most from taking this course?
This course is for anyone who knows a little bit about programming. If you’re really into Machine Learning and want to do more projects to show off your skills, you’ll find this course useful. It’s also great for data scientists who want to use their knowledge to work on real-life problems.
What do I like in the course?
- A lot of mathematical details are covered in the curriculum that give a background of what you are doing instead of simply copying the codes.
- The session was engaging and energetic. Excellent presenters with clear and hands-on explanations
- This course had a perfect balance between practical and theory as compared to other courses where the focus is mostly on theory.
What could have been better?
- There are challenges in understanding the codes as they are outdated. Some sections are left incomplete and amateur.
- Some of the datasets provided are not real-world and unique. It is poor and is not working.
- It will be beneficial to include some unsupervised ML projects covering clustering techniques.
10. Feature Selection of Machine Learning
This course is for people who already know a lot and want to get even better at choosing the important parts of their data. It teaches you how to pick out these parts and build computer programs that are easier to use, work faster, and are easier to understand. You’ll practice with tools like Python and learn different ways to do this. When you finish, you’ll know how to pick the best bits of your data for any project.
Who would benefit most from taking this course?
This course is most appropriate for professionals such as Data Scientists, Software Engineers, and Data Analysts in the following ways:
- Beginner Data Scientists can benefit from learning how to select variables in machine learning. An Intermediate Data Scientist can expand his knowledge and experience with feature selection for Machine Learning. Advanced scientists can take this course to learn about alternative methods for feature selection.
- Software engineers and academics looking to change careers and pursue a new path in data science.
- This course can help Data Analysts improve their skills and advance their careers in data science.
What do I like in the course?
- Feature Learning is one of the most important concepts of machine learning, which is explained comprehensively using different techniques.
- The usage assessment in the real world by the tutor at the end of every chapter is very useful.
- Excellent course with all the dynamics clearly explained and interpreted correctly about Feature Selection methods.
What could have been better?
- The course can include more projects for practical applications.
Useful Post!
Can I learn ML in 1 month?
Learning Machine Learning in just one month can be quite challenging, but it’s not impossible! In this short time frame, you can focus on getting a basic understanding of the concepts and tools used in Machine Learning.
Is ML easier than AI?
Machine Learning (ML) is actually a part of Artificial Intelligence (AI). So, in a way, you can think of ML as a smaller piece of the larger AI puzzle.
Is the Udemy Machine Learning certificate accepted?
Hey Chethna, udemy certificates show that you have completed a course on the platform. You must note that Udemy is not an accredited institution and certificates only show your skills. So, you cannot replace your formal education degree with Udemy certificates.
What is the cost of the ML course in Udemy?
Udemy provides affordable courses in ML which usually costs around USD 50 to USD 200 along with a 30-day money-back guarantee. However, you can get access to these courses at 90% off. The Udemy courses are available for beginners and students can complete them as per their learning pace.
Does Udemy provide good ML courses?
Yes, Udemy provides good courses on Machine Learning. However, you must choose the course carefully, as there are courses for different skill levels. For instance, the courses for beginners will have a different curriculum than those that require prior experience.
Does ML need coding?
In most cases, ML requires prior coding experience. This is because it allows you to write ML algorithms. But, if you are new to coding, you can enroll in a course, and learn programming before learning advanced skills, that are based on coding.
Can I learn ML for free in Udemy?
Students can learn ML for free, but the courses might not provide certificates that demonstrate your skills. Hence, it is always preferable to go for a course, that provides a certificate so that you can create a portfolio and show that to your recruiter.
Is the Udemy Machine Learning certificate accepted?
Udemy certificates show that you have completed a course on the platform. You must note that Udemy is not an accredited institution and certificates only show your skills. So, you cannot replace your formal education degree with Udemy certificates.
What is the main use of machine learning?
The main use of machine learning is to teach computers to learn from data and make predictions or decisions without being explicitly programmed. It’s like giving computers the ability to learn from experience and improve over time, just like humans do.
Can I get a job after learning Machine Learning?
Machine learning can provide job opportunities in tech, healthcare, finance, etc. In this regard, companies always recruit individuals who can analyze data, build predictive models, and uncover insights. So, after developing skills, you can explore roles like data scientist, machine learning engineer, or AI specialist.
How to easily master Machine Learning?
To easily master machine learning, start by grasping the basics: algorithms, data preprocessing, and model evaluation. Then learn Python and practice with libraries like scikit-learn, then advance to TensorFlow or PyTorch. Work on real projects, keep learning from tutorials and stay curious.
Is it mandatory to learn Python for Machine Learning?
Python is popular and widely used in machine learning for its simplicity and extensive libraries (like TensorFlow and PyTorch), However, it’s not strictly mandatory. You can explore other languages like R or even utilize machine learning frameworks in languages like Java or C++.
Is it easy to install AWS and Pyspark?
Installing AWS (Amazon Web Services) and PySpark (Python API for Apache Spark) is easy. However, you will need the right guidance and resources. For this, you have to download and configure the AWS Command Line Interface (CLI) to access AWS services, and then install PySpark using Python’s package manager, pip. After this, you will get instructions from AWS, and PySpark documentation to streamline the process. Also, you might need familiarity with command-line interfaces and Python environments.