The Complete Machine Learning Course with Python (Udemy.com)
Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
Created by: Codestars
Last updated February 2026
What you will learn
- Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
- Solve any problem in your business, job or personal life with powerful Machine Learning models
- Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
- Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc
Quality Score
Overall Score : 90 / 100
Course Description
The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019!
With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them:
Brand new sections include:
Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more.
Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions.
And the following sections have all been improved and added to:
All the codes have been updated to work with Python 3.6 and 3.7
The codes have been refactored to work with Google Colab
Deep Learning and NLP
Binary and multi-class classifications with deep learning
Get the most up to date machine learning information possible, and get it in a single course!
* * *
The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms.
Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival’s “project based" teaching style to bring you this hands-on course.
With over 18 hours of content and more than fifty 5 star ratings, it's already the longest and best rated Machine Learning course on Udemy!
Build Powerful Machine Learning Models to Solve Any Problem
You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
Gain complete machine learning tool sets to tackle most real world problems
Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
Combine multiple models with by bagging, boosting or stacking
Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
Develop in Jupyter (IPython) notebook, Spyder and various IDE
Communicate visually and effectively with Matplotlib and Seaborn
Engineer new features to improve algorithm predictions
Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
Use SVM for handwriting recognition, and classification problems in general
Use decision trees to predict staff attrition
Apply the association rule to retail shopping datasets
And much much more!
No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.
Make This Investment in Yourself
If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!
Take this course and become a machine learning engineer!
This course is eligible for the Codestars Certificate Authority (CCA) certificate. Students can take the official exam via codestarscom, and those who pass the quiz will receive their CCA certificate. (more details in the course!)
Instructor Details
- 4.5 Rating
8,268 Reviews
Codestars
Codestars is a community of instructors behind 100+ courses and over 3 million students worldwide. We were one of the first coding communities to build here, and we've kept building — across web development, mobile, cyber security, and now AI-powered development and automation.
We want to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible. Our courses are built to teach real, applicable skills, taught by instructors who know the subject and know how to teach it.
Together, the instructors that make up the Codestars team create courses on all the topics that students want to learn in the way that students want to learn them: courses that are well-structured, super interactive, and easy to understand.
Whatever you're here to learn — from the fundamentals of programming to the latest in AI-powered development — you're learning from a channel that has already done this at scale, with millions of students, and a track record to show for it.
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Reviews
By Marco Aurelio Vargas Moreno on 3/14/2023
Excelente curso ya que es increíblemente práctico y muy bien explicado. Recomiendo este curso específico como el punto de partida en el conocimiento de aprendizaje automático.
By Pierre Goux on 2/18/2022
Instructive although extremely demanding in terms of advanced statistical concepts : I'd say this learning requires a rather advanced statistical knowledge before hand , and also "some" coding knowledge / for me was looking to obtain knowledge on ML on which I was not too familiar and it is useful although again very complex to follow at times / geared towards pure coders/statisticians as opposed anyone looking at a general understanding of ML & Python
By Pedro Ciancaglini on 9/11/2021
The course is excellent. The examples are amazing, I love reading datasets from libraries, something I didnt know. The only thing I would recommend to the teacher> Slides and links to simulators are missing. I cannot find them. Another thing to add, it would be awesome to include the last 2 sections at the beginning, at least the theorical part of regressions, trees and the rest. For Deep learning, it's logic to have it at the end.
By William Lawrie on 4/15/2021
Extremely simple introduction to the techniques used in Machine Learning. A significant portion of the content is just copy and pasted from the documentation of the sklearn and tensorflow packages. Good if you can code, and have absolutely no idea what ML actually is, or if you desperately need a certificate for some external credits... Would recommend following the MIT open courseware for a free and better introduction, particularly if you're mathematics literate.
By James Mills on 11/21/2020
The instructor basically just goes through lots of examples of using the sklearn package. You'd get the same knowledge from just reading the documentation. He very rarely discusses how the algorithms work and you get the impression he doesn't have a clear understanding of this himself. Where he does talk about when and how you should use the various models, his statistics knowledge is clearly lacking, and the explanations are misleading at best and often simply incorrect (A couple of specific examples below).
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i.e. In section 3.14 the regression plot shown at 6:08 contains an autogenerated regression which and does not have the same results as the regression model just demonstrated (intercept is different). It's not clear if the instructor even realizes this.
The comparison of accuracy metrics for regression against empirical and simulated data in 3.17 is just total nonsense. One could easily construct a simulated dataset with more noise for which the accuracy of the 'near-perfect model' would be worse.
By Cristopher Morales Ubal on 11/14/2020
It is so slow the explanation.
By Ankush Varma on 10/27/2020
GOOD
By Gabriel Burchat on 10/27/2020
Great match so far!
By Jefferson Cowhig on 10/26/2020
Really enjoying the course. Lots of learning. Thank you.
By Krista Beth Davis on 10/25/2020
not able to get anaconda to create enviornment
By Jerry Chan on 10/21/2020
insightful
By Maicon de Menezes on 10/15/2020
A legenda no traduzida de forma adequada









