The Complete Machine Learning Course with Python
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 rating, 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!
Getting Started with Anaconda
The instructions provided are for installation of Anaconda on a Windows OS.
For instruction on how to install Anaconda on a Mac OS, follow this link
In this practice activity / quiz, please:
- Create a new virtual environment
- Calling it "irisproject"
- Ensure that Anaconda, Jupyter Notebook, and Spyder has been successfully installed
Do make sure you have successfully completed the above before you proceed to the next portion. We will need it for the next lecture.