Publish Date - February 3rd, 2023
|Last Modified - May 11th, 2023
Machine learning (ML) is growing in popularity, and with it – languages like R and Python have also exploded in usage and adoption. That being said, it’s incredibly hard to start a career in machine learning due to how many skills you need.
A machine learning engineer will roughly have these types of skills:
- Mathematics (especially statistics)
- programming languages (Python, R, Java, C++)
- problem solving skills
- data visualization and manipulation (SQL, Excel)
- communication skills and presentation skills
- deep understanding of ML concepts like unsupervised learning, neural networks, decision trees and regression models
All of these skills in themselves can be jobs, so it’s no wonder it can be difficult to get into the industry. This is why taking a course on Udemy for machine learning is so fantastic. ML is not an easy concept, but there are hundreds of courses on Udemy that can teach you what it’s all about.
If you’re interested in learning some of the best courses on Udemy for machine learning, read on!
6 best courses on Udemy for Machine Learning
| Category | Reviews and Ratings | Highlights |
| 1.Best course for beginners by Jose Portilla | 4.6 / 5 stars – 126k+ reviews | Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Tensorflow, K-means, Regression (Linear and logistic) |
| 2. Best course for intermediates by Kirill Ermenko | 4.5 / 5 stars – 165k+ reviews | Data pre-processing (R and Python), Regression, Classification, Clustering, Reinforcement learning, NLP, Deep learning, Model selection |
| 3. Best neural network course by Kirill Ermenko | 4.5 / 5 stars – 42k+ reviews | Neural networks, (Convolutionary, Self-organizing, Boltzmann, Auto encoders) |
| 4. Quickest ML course on Udemy by Joshua MacCarty | 4.6 / 5 stars – ~2.8k reviews | No-code ML, use excel to create distributions and regression. Also ML theory without code. |
| 5. Best course for ML projects by Dr. Ryan Ahmed | 4.4 / 5 stars – 2.6k+ reviews | Build a bunch of case studies (Breast cancer, Fashion classification, Credit Card Fraud detection) and much more.. |
| 6. Best NLP course by Lazy Programmer Inc. | 4.6 / 5 stars – 11k+ reviews | Code in Python your own spam detection code and perform latent semantic indexing in Python. Write your own article spinner. |
Best Machine Learning Course (w/ Python) on Udemy for beginners
Python for Data Science and Machine Learning Bootcamp: In this course, students learn the fundamentals of Python programming language and how to apply it to data science and machine learning. It covers topics such as cleaning and preprocessing data, building predictive models, validating results, performing analysis on large datasets, using various libraries in Python like Matplotlib, NumPy, Scikit-Learn, Pandas among others. By the end of the course participants will be able to confidently use Python for data science and machine learning tasks.
Pros:
- I’ve taken many of Jose Portilla’s courses, they’re hands-on, well written and perfect for beginners.
- You’ll actually implement machine learning algorithms, instead of just talking about the theory of them.
- A lot of hands-on data visualization with Matplotlib and NumPy in Jupyter notebooks.
Cons:
- No real AI interaction, you’ll do a lot of of data science and machine learning to help get your self ready to actually dealing with AI.
- This is a pretty old course that hasn’t been updated in a while. The libraries that Jose uses are out dated.
Instructor:
Jose is an expert at what he does. A bonefied data scientist and programmer, he’s well spoken and an expert at his craft. He has multiple tutorials on SQL, Python, Java and other computer science disciplines. He currently is the head of data science at Pierian Training, where he teaches millions of people across the world machine learning models, data analysis and statistics. He’s one of the most popular technology Udemy course teachers.
What his students say:
This course certainly addressed my needs, as it was brimming with useful information that not only expanded my understanding of Python in general, but particularly libraries relevant to data science. Additionally, it introduced me to machine learning concepts and how to implement them in Python. Overall, it was a massive amount of material to digest at one time. I expect to selectively revisit segments of this course moving forward to ensure that I can fully grasp and retain the most relevant subject matter to my task work.
Kennett W. S. – 5 / 5 stars – January 2023
Had a great course with Jose. Even though some of the commands were already outdated, he and his team made it a point to clarify the changes at the start of each project. I also took Data Science and Machine learning with R!
LU JIA JUAN MICHELLE – 5 / 5 stars – January 2023
Verdict:
You can never go wrong with Jose’s courses. As stated before, I’ve done at least 4 courses from Jose and they are top-notch and high quality. You won’t regret taking any of his online courses. His courses always have a way of driving home the skills you need to a fundamental level. The only downside to this course is it hasn’t been updated in quite some time, so it may be lacking in temporal relevancy. That being said, if you’re looking for a good teacher and somewhere to start to learn machine learning, Jose is a great place to start.
Start your machine learning career adventure now!
Best Machine learning Course (w/ Python & R) for intermediates
This course provides an introduction to basic machine learning concepts such as supervised and unsupervised learning algorithms, including regression and classification problems. Participants learn about popular algorithms such as KNN, SVM, Naive Bayes and Random Forest, as well as dimensionality reduction techniques. They also learn best practices for building robust models and how to use these algorithms in the real world.
Pros:
- Over 44 hours of machine learning in R and Python.
- Lots of math which helps with the building of the algorithms
- Tons of in-depth practical vs. theoretical strategies for ML
- 3 hours of Natural language processing
- 6 hours of deep learning
- 5 hours of classification
- Course is modern with updates happening regularly
- You’ll work in Tensorflow (really sought after skill for ML)
Cons:
- This course may be too long, I am struggling to move from chapter to chapter. Only take this if you have the commitment to finish it
- A lot of users complain about the code being “Copy and pasted” which I am experiencing
- There’s 6 hours of math, if you don’t like math – you won’t like this course (and probably shouldn’t be doing ML 🙂 ).
- There are major issues with the code from SVR and onward.
- Due to this course covering so much, the authors end up glazing over concepts which beginners may struggle to know or understand.
Instructor:
Kirill Eremenko, just like Jose Portilla is a very strong data scientist. Drawing on his background in analytics from Deloitte, he has years of years of programming experience in multiple disciplines. With over 52 courses, he has courses on Blockchain, Deep learning, Forex, Business statistics (Logistic regression, multi-linear regression, big data) that are high quality and perfect for learners.
What his students say:
I have done other courses on machine learning/data science, and this is definitively one of the best, and even better, it is faster than much of the others. It would be even better if they used more examples.
Lucas Moreira M. – 4.5 / 5 stars – January 2023
Amazing course and amazing explanations from both creators! Thank you for producing this excellent 5+ star material! Very practical, clear, and informative.
Jonathan K – 5 / 5 stars – January 2023
Verdict:
Kirill’s courses will never let you down. While there may be some prerequisite skills (statistics, data manipulation in Python, SQL or R) required for these courses, you’ll get a lot of hands-on experience. Only downside to his courses are they seem to be centered around textbook like answers. This means you’ll never handle or tackle machine learning projects of substance in his course.
Continue your ML career with Kirill Eremenko’s top rated course!
Best Deep learning for Artificial Neural Networks course on Udemy
This course introduces participants to deep learning, a subset of machine learning that focuses on training powerful neural networks to make accurate predictions based on data. Students gain hands-on experience with popular deep learning frameworks such as TensorFlow and Keras. In addition, they learn advanced concepts such as convolutional neural networks (CNNs) for image recognition, long short term memory (LSTM) networks for natural language processing, and recurrent neural networks (RNNs) for sequence analysis.
Pros:
- It’s another Kirill course, so you know it’s going to be in-depth and high quality, full of programming knowledge.
- Despite being specifically about Neural networks, this course has roughly 4 hours at the end on Machine learning concepts (Regression, classifiers, data preprocessing, logistic regression)
- There’s a case study in part 4 of self-organizing maps
- Usage of PyTorch for Boltzmann machine
Cons:
- This course covers a lot of topics for neural networks. Maybe too many, since it can be overwhelming to learn so many concepts at once.
- This course reminded me of some courses on Coursera. They have good content, a solid instructor – but absolutely blow through topics without explaining them to a new learner. It could also be my skill level at artificial intelligence is weak, so I need to practice more.
- Reinforcement learning in the ML portion of this course would have been nice ending as it’s heavily related to neural networks.
Instructor:
Kirill is a solid instructor, as stated before in the previous course. That being said, this is an advanced topic that he has a lot knowledge on, so you can’t go wrong learning from him.
What do his students say?
The course got me into data science! It is a very good course, some lectures are repetitive and feel slow at at times but I do understand that some concepts are quite hard, the team do a excellent work at explaining these though.
Pedro Santa R. – 4.5 / 5 stars – December 2022
Supervised neural networks were well explained; very interesting the parts about SOMs and Boltzmann Machines, AutoEncoders could have been explored further particularly aiming to extract image embeddings
Flavia P. – 4 / 5 stars – December 2022
Verdict:
Great course with a lot of hands-on python, while being extremely high level at the same time. If you’re looking to understand a little more on deep neural networks (specifically) and less on general machine learning, Kirill’s course is a solid choice for you. This course does a great job showing how to calculate K-means with Python :).
Check out the best neural network course on Udemy
Best and shortest machine learning course on Udemy – Machine Learning for Data Analysis: Data Profiling & QA
This course introduces participants to the fundamentals of machine learning and its applications in biomedical data. It covers topics such as data preprocessing, evaluation metrics, feature engineering and selection, supervised learning algorithms such as k-nearest neighbors and decision trees, unsupervised learning algorithms such as clustering methods and anomaly detection. In addition, it provides hands-on experience with popular machine learning libraries such as Scikit-Learn and TensorFlow.
Pros:
- No code!! Omg, a No code ML course.
- Purely excel driven course, with emphasis on understanding ML and it’s tools before you dive into Python and R.
- This course is super short and can be consumed in one afternoon.
Cons:
- More like a theory ML course, definitely less hands-on.
- Course is extremely short, and may almost feel like you’ve been ripped off.
Instructor:
Maven Analytics is the umbrella company, but Joshua MacCarty is the teacher for this course. As a machine learning engineer of 10 years, he definitely has the credentials to teach these types of courses. He worked as an analytics at Philips and PNC bank for many years. Therefore, while he doesn’t have an immensely impressive pedigree, he still has the experience to provide expertise and specialization to the ML subject.
What do the students think?
Super cool: Easy to follow and to grab the knowledge. Thanks for the pdf book, which is very helpful to make quick notes in between.
Jeffrey S. – 5 / 5 stars – January 2023
Clear & easy-to-understand even for a non-native English speaker who majored in Language studies back in university…+ Hands-on practice projects to really tune your skills. What else can you ask for! 🙂
Yi Z. – 5 / 5 stars – November 2022
Verdict:
If you’re looking for a quick and easy way to start your Machine learning adventure, look no future. This course requires no code and has solid examples to help drive home key concepts of ML.
Best Udemy course for Machine Learning projects
This course teaches students how to apply theoretical concepts of machine learning to real world problems using Python programming language. Participants learn best practices for building robust models such as cross validation and regularization techniques. They also gain hands-on experience with several popular libraries such as NumPy, Scikit-Learn and Pandas.
Pros
- You’re in for a treat, Ryan Ahmed is a fantastic teacher, professor and engineer. He’s very passionate about all things tech and has multiple peer reviewed papers on ML.
- 6 projects, 6 concepts just over 9 hours. If you’re ambitious, you can do this in one day.
- The breakdown of the machine learning projects and well structured, similar to something you would see in a Stanford comp sci. textbook.
- Lots of deep learning with python in the “Credit Card Fraud Detection” project.
Cons
- This course is not for beginners.. You need to know Python to a relatively proficient degree to make optimal use of this course. Knowing R programming is also a benefit as well.
- It would have been nice if this course had a section on deploying the code Amazon web services (AWS) or deploying it to some sort of environment. Having the programs on your computer is great, being able to interact with them is better.
Instructor:
An engineer through and through, Dr. Ryan Ahmed has a strong background in artificial intelligence, Cloud projects (AWS and Google cloud), network engineering and Python. You won’t find a better teacher on Udemy for the price range that these courses are.
What do his students think:
This course exposes to real-life case studies such as Breast Cancer. It also explains how Machine Learning is being used in life and indeed lives up to the name of the course! Thank you Ryan, Ligency 1, Ligency and Rony!
EDWALD NEO WEE TAT – 5 / 5 stars – January 2023
Really great course for people who already had begun their journey with ML but want to expand their horizons
Anna Z. – 4.5 / 5 stars – January 2023
Verdict
Solid course, albeit a few subjects are lacking. You can add 6 projects to your ML Github portfolio. If you’re building a website, it may also be cool to create a UI for this project and have it work in a production environment.
Best NLP course on Udemy – Data Science: Natural Language Processing (NLP) in Python
This course focuses on natural language processing (NLP) using the Python programming language. It covers topics such as text preprocessing and cleaning, tokenization, part-of-speech tagging, sentiment analysis, topic modeling and text classification. Participants learn best practices for building robust NLP models and how to use popular libraries like NLTK, spaCy and Gensim for their projects. By the end of the course they will be able to confidently perform data science tasks with natural language data.
Pros:
- Lots and lots of Python
- Get to play around with different Markov models and build your own article spinner
- Create a spam detection script and also gain understand of LSI (latent semantic indexing), which is a great tool for understanding search engine optimization.
- He actually adds sections if students request help in the comments overwhelmingly.
Cons:
- No Python boocamp, you need to know multiple things like NumPy, Scipy, Scikit and Matplolib + scraping with Beautiful Soup. Therefore, you need to have a solid understanding of Python to make good use of this r course.
- Need to be good with statistics as well.
Instructor:
While it’s a team of programmers, the courses that Lazy programmer develop are high quality and in-depth. I’ve never seen an instructor add sections when requested by students. They have over thirty courses revolving around Tensorflow, ML, NLP and forecasting.
What do his students think:
Quite a good course, recommended with no doubt at all. The only disadvantage is that for someone who is a beginner like me, it may be a bit difficult to learn some mathematical prerequisites. Nevertheless the course is useful even without mathematical knowledge.
Juan S. – 5 / 5 stars – January 2023
Yes I have really enjoyed the course. It was very wonderful experience learning the NLP course. Lazy Programmer’s intuitive teaching method was so good that made me able to grasp the concepts even though as a beginner to NLP.
Anjan R. – 5 / 5 stars – January 2023
Verdict
I bought this course and can’t wait to take it. It’s got a good mix of projects, hands-on work and theoretical vs. practical. If you’re looking to learn NLP, look no further!
Pick up the best NLP course on Udemy Now!
Conclusion
I hope these courses help you in looking for ML courses to further your career. The courses mentioned provide an excellent foundation in machine learning and data science for anyone looking to gain a deeper understanding of the topics. Whether you are just starting out or are an experienced practitioner, you can be sure that you will benefit from the knowledge imparted in these courses. From cleaning and preprocessing data to building robust models to performing analysis on large datasets, these course offerings have something for everyone! Even more, by using popular libraries such as NumPy, Scikit-Learn, Pandas and TensorFlow students can easily apply their skills to real world problems. So dive into one of these courses today and take your data science skills to the next level!