Udemy is an excellent marketplace for affordable, comprehensive courses on all manner of subjects. The ones available for those looking to learn machine learning are no different.
However, with all that choice it can be difficult to know what course to dive into and take. Well, we are here to help.
In this top 10 review round-up, we have carefully selected 10 of the best machine learning courses currently running on Udemy in 2021. Whether you are looking for a fully intensive course or a beginner-friendly option to test the waters you will find something here to suit you.
For our top choice machine learning courses at a glance, just head to the table below. For an in-depth review of each of the selected courses, keep on reading.
Table of Contents
Best Machine Learning Courses on Udemy
COURSE | TITLE | DETAILS | OUR RATING | |
 | BEST COURSE FOR BEGINNERS Machine Learning For Complete Beginners | 4hrs of video | ||
 | Machine Learning, Data Science & Deep Learning | 14hrs of Video | ||
 | BEST PRACTICAL COURSE Deep Learning Projects Master Class | 5.5hrs of video | ||
 | BEST FOR NON-CODERS Machine Learning No-code Approach | 2.5hrs of video | ||
 | BEST FOR INTERMEDIATES AWS Certified Machine Learning | 9.5hrs of video | ||
 | Feature Engineering for Machine Learning | 10.5hrs of video | ||
 | CURRENT BESTSELLER Building Systems with Machine Learning AI | 10.5hrs of video | ||
 | Feature Selection for Machine Learning | 5hrs of video | ||
 | BEST FOR EXPERTS AWS Machine Learning Certification Exam | 17hrs of video | ||
 | Machine Learning & AI with Support Vector | 9hrs of video |
The Reviews
These courses have been chosen in order to provide a broad base of options from beginner machine learning enthusiasts right up to working professionals.
Best Machine Learning Courses for Beginners
1. TOP PICK: Machine Learning for Absolute Beginners
This machine learning course is designed to use Python programming language and JupyterLab development Tool and Pandas Library for executing data science tasks.
It is a follow-up of the Level 1 ML course for beginners that is offered by the same tutor, Idan Gabrieli. It introduces the learner to Pandas, which is a Data science library, as well as trains him/her on how to select, filter, clean, group, sort, and export data.
Moreover, one is taught how to load and analyze tabular datasets. This is the best machine learning course in this review for beginners.
This 4-hour long course has 6 sections that taught in 42 lectures. The course offers 4 hours of on-demand videos, an article, and 5 downloadable resources.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in February 2021. To date, more than 20,900 students have completed the course.
- Target Audience: Beginners in data science, ML engineers, and AI professionals.
- Course Type: Tutorials.
- Rating: 4.6 (out of 5) from 121 ratings.
- Language: This course is offered in English, and the video lectures feature autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Recommended that the learner completes the aforementioned Level 1 ML course.
The Tutor
Idan Gabrieli is a software solution and AI expert who has worked with cloud systems.
Content
- Installation of Anaconda and JupyterLab
- Python for Data Science.
- Pandas library.
- DataFrame operations and Data Transformation.
- Data Cleaning.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
2. Machine Learning, Data Science, and Deep Learning with Python
This machine learning and data science course is designed to use Python programming language, Keras, TensorFlow, and Apache Spark for algorithm training using deep learning.
It introduces the learner to MLLib for ML implementation, and Seaborn and MatPlotLib for data visualization, as well as trains him/her on how to build an artificial neural network.
The learner is also trained on how to classify data using support vector machine, decision trees, and K-Means clustering, in addition to using deep learning to classify data and images.
Moreover, the learner is expected to use collaborative filtering to develop an ML system – the movie recommender system, as well as use reinforcement learning to develop a Pac-Man bot.
As expected, the learner is taught how to select, filter, clean, group, sort, and export data.
This course has 12 sections that are taught in 111 lectures. It is expected to last for about 14.5 hours. The course offers 14.5 hours of on-demand videos, 6 articles, and 3 practice projects.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in February 2021. To date, more than 143,100 students have completed the course.
- Target Audience: Beginners in data science, ML, and AI.
- Course Type: Tutorials and practice projects.
- Rating: 4.6 (out of 5) from 24,301 ratings.
- Language: This course is offered in English, and the video lectures feature autogenerated Polish, Italian, and Portuguese subtitles.
- Requirements: Personal Computer (PC) connected to the internet. High school mathematics skill. Basic scripting skills.
The Tutor
The instructor who prepared and teaches this course is Frank Kane, who has been described in the review of the course titled, Taming Big Data with Python and Apache Spark.
Content
- Installation of Anaconda and TensorFlow 2.0.
- Python for Data Science.
- Predictive models.
- Data mining.
- Recommender systems.
- Apache Spark.
- DataFrame operations and Data Transformation.
- Deep learning.
- Artificial neural network.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
- Related Content: A Short Introduction to Machine Learning
3. BEST PRACTICAL COURSE: Deep Learning Projects Masterclass 2021
This machine learning course is designed to use Python programming language for algorithm training via deep learning, and then building AI web apps that can be deployed using StreamLit and Heruko.
The learner is taught how to use deep learning to develop the following applications – a Pan Card Tempering Detector, Image Watermarking, Text Extraction, and Traffic Sign Classification apps, as well as 3 prediction apps for predicting bird species, plant diseases, and dog breeds.
As expected, the learner is taught how to build ML models and perform exploratory data analysis. This is the best practical machine learning course in this review for beginners.
This course has 11 sections that are taught in 64 lectures. It is expected to last for about 6 hours. The course offers 5.5 hours of on-demand videos, 10 articles, 19 downloadable resources, and the aforementioned practice projects.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in February 2021. To date, more than 730 students have completed the course.
- Target Audience: Beginners in data science, ML, and AI who are knowledgeable in basic Python programming.
- Course Type: Tutorials and practice projects.
- Rating: 4.8 from 39 ratings.
- Language: This course is offered in English.
- Requirements: Personal Computer (PC) connected to the internet. Basic Python programming skills.
The Tutor
This course is prepared and taught by Pianalytix Edutech Pvt Ltd, a company that uses AI to drive innovative product designs.
Content
- Installation of Flask app.
- Build Pan Card Tempering Detector app.
- Build Image Watermarking app.
- Build Text Extraction app.
- Develop Traffic Sign Classification app.
- Develop bird species prediction app.
- Develop plant diseases prediction app.
- Develop dog species prediction app.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
4. BEST FOR NON-CODERS: Machine Learning No-Code Approach
This machine learning course uses Azure ML Studio that provides a simple drag-and-drop environment for building and deploying ML models that can be used for supervised machine learning and algorithm training.
This course requires no coding experience, and there will be no coding done as the ML Studio used uses the visual programming paradigm to create ML models.
This paradigm allows the learner to explore supervised ML and predictive models, as well as using test these models using the Titanic dataset. This is the best machine learning course in this review for non-coders.
This course has 6 sections that are taught in 42 lectures. It is expected to last for about 3 hours. The course offers 2.5 hours of on-demand videos, an article, and 7 downloadable resources, as well as assignments.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in August 2020. To date, more than 3,900 students have completed the course.
- Target Audience: Beginners in data science, ML, and AI.
- Course Type: Tutorials and practice projects.
- Rating: 4.6 from 248 ratings.
- Language: This course is offered in English, and the video lectures feature an autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Knowledge of working with spreadsheet app like Google Sheets or Microsoft Office Excel. Azure ML Studio subscription.
The Tutor
This course is prepared and taught by Aderson Oliveira and Scott Duffy, in collaboration with the company, Software Architect.ca.
Scott Duffy is an Azure certified architect and developer of Azure systems, and Aderson Oliveira is an accomplished software developer who has worked with the .NET Core Framework and Azure Systems.
Content
- ML categories.
- Azure ML Studio.
- Predictive models.
- Titanic dataset.
- Regression model.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
Best Machine Learning Courses for Intermediates
5. TOP PICK: AWS Certified Machine Learning Specialty 2021
This ML certification preparation course uses Amazon Web Services (AWS) to teach the learner the basis of cloud engineering techniques and data engineering using DynamoDB, Kinesis, Glue, and Amazon S3.
The course prepares the learner to take the AWS Certified ML specialty exam.
It teaches the learner exploratory data analysis using Apache Spark, EMR, Athena, and scikit_learn, as well as High-level ML services including Comprehend, Rekognition, Polly, Lex, Transcribe, and Translate; in addition to how to secure ML pipelines.
The learner is also taught about deep learning, L1 regularization, deep neural networks, and automatic model tuning using Amazon SageMaker. This is the best machine learning course in this review for intermediate ML students.
This course has 7 sections that are taught in 114 lectures. It is expected to last for about 10 hours. The course offers 9.5 hours of on-demand videos, 2 articles, and a practice test.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in February 2021. To date, more than 23,350 students have completed the course.
- Target Audience: Intermediate ML students who seek AWS certification for using AWS to perform data science tasks.
- Course Type: Tutorials and a practice test.
- Rating: 4.5 from 3,303 ratings.
- Language: This course is offered in English, and the video lectures feature an autogenerated English and French subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Knowledge of ML basics. Existing AWS account. Associate-level knowledge of Amazon AWS.
The Tutor
This course is prepared and taught by Stephane Maarek and Frank Kane (mentioned in a previous review), in collaboration with Sundog education that is run by Kane.
Stephane Maarek is an AWS certified DevOps professional and solutions architect expert, as well as an expert in Apache Kafka.
Content
- Amazon AWS, DMS, and S3.
- Data Engineering.
- Exploratory data analysis.
- ML operations.
- ML modeling.
- ML implementation.
- Deep learning with EMR and EC2.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
6. Feature Engineering for Machine Learning
This ML course focuses on data transformation and building ML models for algorithm training, and it uses Python programming and Jupyter Notebooks.
The learner is taught different ways of handling rare and unseen categories, as well as how to use techniques of data imputation to obtain missing data, besides conversion of variable values to discrete values.
Likewise, the learner is trained on how to preprocess and transform data, as well as remove outliers from data sets.
This 11-hour long course has 14 sections that taught in 134 lectures. The course offers 10.5 hours of on-demand videos, 21 articles, 4 downloadable resources, and an assignment.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in January 2021. To date, more than 11,430 students have completed the course.
- Target Audience: Data scientists and software engineers.
- Course Type: Tutorials and an assignment.
- Rating: 4.7 from 1,766 ratings.
- Language: This course is offered in English, and the video lectures feature autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Basic Python programming knowledge. Basic understanding of ML and Scikit_Learn.
The Tutor
Soledad Galli is a data scientist who has developed successful machine learning models for training fraud detection, credit risk, and insurance claims algorithms. She graduated with a Ph.D. in Biochemistry.
Content
- Types and Characteristics of Variables.
- Imputation of missing data, including multivariate data.
- Variable transformation.
- Outlier handling.
- Categorical variable encoding.
- Discretization.
- Feature scaling.
- Engineering DateTime and mixed variables.
- Assembling an engineering data pipeline.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
7. CURRENT BESTSELLER: Building Recommender Systems with Machine Learning & AI
This ML course is prepared and taught by Frank Kane, and it focuses on how to apply collaborative filtering, neural networks, deep learning, Restricted Boltzmann Machines, and Apache Spark to build an ML model for training algorithm that can be used to develop a working AI system, such as a recommender system.
The recommender system that is developed by the learner during the course is built using matrix factorization and supports session-based recommendations.
This is the current bestseller ML course in this review for intermediate ML students.
As it can be expected with Machine Learning courses prepared by Frank Kane, this course uses Python programming language, and the learner is taught how to select, filter, clean, group, sort, and export data.
Moreover, the learner uses SVD and SVD++ when building the system.
This course has 14 sections that are taught in 122 lectures. It is expected to last for about 11 hours. The course offers 10.5 hours of on-demand videos and 3 articles.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in February 2021. To date, more than 11,900 students have completed the course.
- Target Audience: Data scientists and intermediate ML students.
- Course Type: Tutorials.
- Rating: 4.4 from 1,652 ratings.
- Language: This course is offered in English, and the video lectures feature autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Basic computer science knowledge. Basic scripting skills.
The Tutor
The instructor who prepared and teaches this course is Frank Kane, a renowned data scientist.
Content
- Build recommendation engine.
- Build ensemble recommenders.
- Evaluate Recommender systems.
- Apache Spark.
- DataFrame operations and Data Transformation.
- Deep learning.
- Matrix factorization methods.
- Content-based filtering and neighborhood-based collaborative filtering.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
8. Feature Selection for Machine Learning
This ML course is prepared by Soledad Galli, and it focuses on data transformation and how to choose variables in data that can be used as data features when building ML models for algorithm training.
It uses Python programming and Jupyter Notebooks. The learner is taught how to filter data, embed it with features, and wrap it for processing in the Machine Learning model.
As expected, the learner is taught feature selection techniques, including how to use decision trees, lasso, and hybrid methods to select features, as well as analyze and comprehend each feature and its utility.
This 5.5-hour long course has 12 sections that taught in 80 lectures. The course offers 5 hours of on-demand videos, 16 articles, and a downloadable resource.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in December 2020. To date, more than 8,250 students have completed the course.
- Target Audience: Data scientists and software engineers.
- Course Type: Tutorials.
- Rating: 4.6 from 1,238 ratings.
- Language: This course is offered in English, and the video lectures feature autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Basic Python programming knowledge, including use of Numpy and Pandas. Basic understanding of ML and Scikit_Learn.
The Tutor
Soledad Galli is a data scientist who graduated with a PhD in Biochemistry.
Content
- Feature selection.
- Filter methods – basics, correlation, statistical measures, and other methods.
- Wrapper methods.
- Embedded methods.
- Hybrid feature selection methods.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
Best Machine Learning Courses for Experts
9. TOP PICK: AWS Machine Learning Certification Exam
This is a complete guide to preparing for the AWS ML certification examination, and it comes with more than 200 questions and 500 slides that cover topics that are examined including data engineering, ML, deep learning, Kinesis services, AWS SageMaker, feature engineering, neural topic modeling, and Python libraries such as MatplotLib, Seaborn, Numpy, Pandas, and scikit_learn.
This course can be taken to gauge how well one has understood the AWS Certified Machine Learning Specialty 2021 course offered by Stephane Maarek and Frank Kane.
As expected, this exam guide reviews the basics of cloud engineering techniques and data engineering using DynamoDB, Kinesis, Glue, and Amazon S3.
It also reviews exploratory data analysis using Apache Spark, EMR, Athena, and scikit_learn, as well as High-level Machine Learning services including Comprehend, Rekognition, Polly, Lex, Transcribe, and Translate.
This is the best machine learning course in this review for ML students who seek AWS ML certification.
This course has 13 sections that are taught in 172 lectures. It is expected to last for about 17 hours. The course offers 17 hours of on-demand videos, 26 articles, and 13 downloadable resources.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in July 2020. To date, more than 5,060 students have completed the course.
- Target Audience: ML engineers who seek AWS certification for using AWS to perform data science tasks.
- Course Type: Tutorials.
- Rating: 4.5 from 616 ratings.
- Language: This course is offered in English, and the video lectures feature an autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. Basic knowledge of ML and AI. Existing AWS account. Associate-level knowledge of Amazon AWS.
The Tutor
This course is prepared and taught by Dr. Ryan Ahmed and Mitchell Bouchard, in collaboration with the Ligency Team.
Dr.Ahmed is a mechanical engineer who specializes in using AI for fault detection. Mitchell Bouchard has a degree in computer graphics and has worked as a filmmaker.
The Ligency Team helps data scientists create good online courses.
Course Content
- Amazon AWS, DMS, and S3.
- Data Engineering.
- Exploratory data analysis.
- ML operations.
- ML modeling.
- ML implementation.
- Deep learning.
- Amazon data migration.
- Feature engineering.
- AWS Kinesis services.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
10. Machine Learning and AI with Support Vector Machines
This machine learning and data science course use Python programming language to develop regression and classification algorithms that can be used in ML models (for algorithm training) and AI.
The learner is taught how to build a support vector machine (SVM) that can be extended into a neural network or radial basis function (RBF) network.
Most importantly, the learner is taught the theoretical basis of SVM, as well as how to derive its (SVM) kernel using Lagrangian Duality and how quadratic programming is used with SVM. Additionally, the learner is taught about Polynomial, Sigmoid, and Gaussian kernels, as well as Support Vector Regression.
This course has 12 sections that are taught in 73 lectures. It is expected to last for about 9 hours. The course offers 9 hours of on-demand videos.
Afterward, the learner is awarded a certificate of completion as proof that (s)he completed this course. This course was updated in November 2020. To date, more than 6,240 students have completed the course.
- Target Audience: ML and AI engineers.
- Course Type: Tutorials.
- Rating: 4.6 from 748 ratings.
- Language: This course is offered in English, and the video lectures feature autogenerated English subtitles.
- Requirements: Personal Computer (PC) connected to the internet. High school mathematics skill. Know-how of logistic regression. Python programming and skills in using Numpy.
The Tutor
The instructor who prepared and teaches this course is the Lazy Programmer Team, which is the trade name used by a data scientist and ML engineer who works as a full-stack software engineer.
Content
- Python for ML and AI systems.
- The theoretical basis of SVM.
- Linear classifiers.
- Linear SVM.
- Kernel methods.
- Duality.
- Neural networks.
- SVM extensions and implementations.
After one registers for this course, (s)he is given full lifetime access to the course resources. The course content can be accessed using a PC, smartphone, Smart TV, and internet-enabled mobile devices.
References
- “Machine learning for middle-schoolers: Children as designers of machine-learning apps,” H. Vartiainen, T. Toivonen, I. Jormanainen, J. Kahila, M. Tedre and T. Valtonen, (2020) IEEE Frontiers in Education Conference (FIE), pp. 1-9, doi: 10.1109/FIE44824.2020.9273981. [Link]
- “Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs.” Alshehri, M., Alamri, A., Cristea, A.I. et al. Int J Artif Intell Educ (2021). https://doi.org/10.1007/s40593-021-00246-2
- “Usage of Massive Open Online Course to Motivate Student in Learning,” D. L. Kusumastuti and V. U. Tjhin, (2021) 6th International Conference on Inventive Computation Technologies (ICICT), pp. 1075-1078, doi: 10.1109/ICICT50816.2021.9358731. [Link]
- “The Construction of Undergraduate Machine Learning Course in the Artificial Intelligence Era,” W. Sun and X. Gao, (2018) 13th International Conference on Computer Science & Education (ICCSE), pp. 1-5, doi: 10.1109/ICCSE.2018.8468758. [Link]
