The Complete Machine Learning Course with Python (

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

Created by: Codestars by Rob Percival

Produced in 2022

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

Content Quality
Video Quality
Qualified Instructor
Course Pace
Course Depth & Coverage

Overall Score : 70 / 100

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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 practicesavailable 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 extrations.
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:
  • Set up a Python development environment correctly
  • 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!Who this course is for:
  • Anyone willing and interested to learn machine learning algorithm with Python
  • Any one who has a deep interest in the practical application of machine learning to real world problems
  • Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
  • Any intermediate to advanced EXCEL users who is unable to work with large datasets
  • Anyone interested to present their findings in a professional and convincing manner
  • Anyone who wishes to start or transit into a career as a data scientist
  • Anyone who wants to apply machine learning to their domain

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Instructor Details

Codestars by Rob Percival

Best-selling Udemy instructor Rob Percival wants to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible. But as just one man, Rob couldn't create all the courses his students - more than half a million of them - wanted.
That's why Rob created Codestars. 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. Codestars wants to make it as easy as possible for learners of all ages and levels to build functional websites and apps. Anthony Ng has spent the last seven years as a Senior Lecturer teaching algorithmic trading, financial data analysis, banking, finance, investment and portfolio management. He assists Quantopian, a Boston-based Hedge Fund, to conduct Algorithmic Trading Workshops in Singapore and has presented in the recent QuantCon Singapore 2016. You can find his Algorithmic Trading tutorials on his YouTube channel. Just click the YouTube icon to visit his channel.
Passionate with finance, data science and python, Anthony enjoyed researching, teaching and sharing on these topics. Anthony studied Masters of Science in Financial Engineering at NUS Singapore.
Hi! I'm Rob. I have a degree in Mathematics from Cambridge University and you might call me a bit of coding geek.



48 total reviews

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This is the course I have been waiting for... amazingly practical, cleverly explained. I strongly recommend this specific course as the most important starting point in your machine learning journey..

I came to this course for a search on LASSO, the introduction to the course initially was very good but hen it went deeper especially in visualization, I struggled a bit in understanding. Also in the regression some topics were brought in telling that the explanation on the same will be provided later.The coverage of the course is good, but sometimes we get lost in trying to search for some terms/code and that takes much time. Rather than just going on to the jupyter notebook in the later part, the course would have done better on a nice illustration on a whiteboard and then comeback on the jupyter notebook on code illustrations

Hands on experience and study helped me in getting into the programming and machine learning both. thanks

In terms of libraries and basic concepts it was good . But concepts like NLP was totally missing . Also i felt the absence of some math being explained like gradient descent

I enjoyed the course and learned a lot how we can apply various ML methods using scikit learn. But this is just beginning. I will play with all the methods we covered in this course and start applying them on my projects. Thank you for providing useful material that is of great help.

there should be 1 exercise in every model to understand how the algorithm actually works. the course is good for beginner and provide good basic understanding.

It was tough at first, but able to adapt quickly as instructor walkthrough is very detailed. very engaging as it prompt us to experiment the cariables ourself so as to see how each of them affect the result

I dont c any good projects being done..its just not up to the mark

All in all good course. Quite a lot of repetition though. Some videos could have been shorter.

It was a great course and it gave me a good hang of all the topics covered.

The instructor doesn't really explain most of the code he uses. Visualizations are pretty complex and not clearly broken down. I wouldn't recommend this course for a novice.

I don't know what's happening but in Q&A I don't have any answer at all some codes are not working at all the cod is not updeted but at least the explanation of theory is good.Good for theory but not updated code