The Complete Machine Learning Course with Python

Category: Tutorial


Posted on 2019-01-26, by everest555.

Description


The Complete Machine Learning Course with Python
MP4 | Video: 1280x720 | Duration: 18 Hours | 3 GB | Subtitles: VTT | Project Files
Author: Codestars by Rob Percival | Language: English | Skill level: All Levels


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

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 !

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
Combine multiple models with by bagging, boosting or stacking
Make use to unsupervised Machine Learning (ML) algorithms to understand your data
Develop in Jupyter (IPython) notebook, Spyder and various IDE
Communicate visually and effectively with MatDescriptionlib 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
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

Requirements:
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.
Basic understanding of high school level math and algebra

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